Practitioners Unplugged

IndustrialSage
  • 5.0 (4)
  • TECHNOLOGY
  • UPDATED MONTHLY

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.

  1. FEB 3

    Episode #17 | Open Automation Is Finally Happening: Real-World Proof from Universal Automation

    For years, the automation industry talked about decoupling hardware and software. IT/OT convergence became a buzzword that everyone nodded at but few truly believed would arrive. Episode 17 of Practitioners Unplugged, recorded live at Schneider Electric’s Innovation Summit in Las Vegas, proves the skeptics wrong. Greg Boucaud, Chief Marketing Officer of UniversalAutomation.org, and Renato Silva, CEO of Aimirim, shared real implementations, actual deployments, and measurable results. This wasn’t a vision presentation. Instead, it was a field report from practitioners who’ve already made the future work. The Universal Automation Movement: Four Years of Real Progress UniversalAutomation.org launched in November 2021 with nine founding members. Four years later, the nonprofit association has grown to 115 members spanning end users, system integrators, technology vendors, and academia. “Our motto in life is to unlock the automation software—decouple hardware and software in the automation space,” Greg explained. While the goal sounds simple, it represents a fundamental shift in how industrial automation functions. Think about your personal computer. You download software, install it, and never wonder about your laptop brand. Industrial automation historically hasn’t worked this way. Instead, you needed the right brand, the correct hardware, and the proper topology. Through open standards—specifically IEC 61499—UniversalAutomation.org brings the IT world’s flexibility to OT environments, enabling true hardware/software decoupling. Why Decoupling Matters for Long-Term Investment Greg highlighted a critical challenge: “If you invest in your OT systems, your investment is tightly tied to the lifecycle of your hardware. If your hardware tends to be end of life, then you need to reinvest for the next generation.” That’s an expensive, disruptive cycle. When hardware reaches obsolescence, companies face massive capital expenditures to replace entire systems. However, decoupling changes this equation dramatically. Software becomes independent from hardware lifecycles. Manufacturers can upgrade hardware without throwing away automation logic. Furthermore, they can add new capabilities without rip-and-replace projects. For facilities running 20, 30, even 50 years, this matters enormously. Consequently, the ability to evolve automation without complete overhauls transforms how organizations approach digital transformation. The Three-Pillar Community What makes UniversalAutomation.org different? A deliberate community structure bringing together three critical groups. Users of automation represent the entire value chain—end users, system integrators, technology integrators like Aimirim, and distributors. Technology vendors include both traditional automation companies and increasingly IT companies attracted by easier data access. Academia completes the picture by training the next generation with full curriculum provided by UniversalAutomation.org. Greg cited a sobering statistic: “By 2030, 2 million jobs will go unfilled in the automation space because of lack of engineering skills.” Therefore, academia’s role in closing this gap becomes strategic, not optional. The Aimirim Story: Nine Years to What Usually Takes Thirty Renato Silva’s journey provides the perfect case study for why open standards matter. Aimirim specializes in advanced process control based on artificial intelligence and real-time optimization. Here’s what makes the story remarkable: Aimirim accomplished in nine years what typically takes established companies 30-40 years. Moreover, they achieved this by building on the IEC 61499 framework. “Would it be possible to do in 90 years the same thing we do without a framework for scaling? No,” Renato emphasized. “The scalability of complex technology is only feasible right now because of the standard.” The British American Tobacco Deployment Aimirim’s flagship implementation with British American Tobacco (BAT) demonstrates open automation’s practical viability. Starting in Uberlândia, Brazil, Aimirim digitalized BAT’s entire industrial operation using IEC 61499 and Universal Automation Organization (UAO) runtime. What started locally scaled globally. Consequently, the same technologies now operate across at least eight countries for BAT, proving the approach works across different regulatory environments. “We could deploy an advanced process control in one week,” Renato explained. “And then they say, can you scale to 15 more facilities?” For startups competing against 30-40 year old companies, the open standard framework became the competitive differentiator. How It Works: The Edge Layer Transformation Dante asked the technical question practitioners need answered: what compelled choosing IEC 61499 over traditional PLCs and SCADA infrastructure? Renato’s explanation reveals the architectural shift: “The point about the PLC and DCS systems for us is that it became purely infrastructure. We take some edge device—it doesn’t matter the brand—and we fully integrate this edge device to any PLC.” Traditional PLCs lack edge compute power for complex AI models. In contrast, Aimirim’s approach integrates edge compute at the same layer, treating existing PLCs as infrastructure. Maintaining Safety While Adding Capability “If we can read, I can take this information and stream it to a cloud,” Renato explained. “If I can write, I can take some decisions on the edge and instantly write on the PLC.” Critically, this approach maintains safety: “When the PLC becomes infrastructure, we keep the automation as they are. If you turn off my technology, you instantly go back to your old technology.” As a result, this architecture enables brownfield deployments without ripping out existing systems. Organizations add capabilities on top of current infrastructure, upgrading incrementally. What End Users Actually Care About Renato shared an insight from driving 120,000 kilometers per year for three years, visiting facilities and listening to operators, supervisors, managers, and executives: “The client doesn’t want one standard or another. They just don’t care. They want to know if you as a company are compliant. They want efficiency increase.” This perspective matters. Technology providers often sell features and standards. However, end users buy outcomes and compliance. Sree highlighted the strategic thinking behind standards: “You were able to think two, three steps ahead. The client wants scalability. The value of standards in enabling that.” End users struggle to envision the end state of their digital transformation journey. Therefore, practitioners and vendors thinking strategically understand that standards enable the scalability clients need even when they can’t articulate it. Proving Capability Through Demonstration “It’s a game of proving everything you say,” Renato noted. “You go to a big client and you say, I can deploy an advanced process control in one week. And they say, okay, do it.” The proof comes through demonstration, not presentation. Startups especially must prove capability before negotiating terms. Moreover, standards enable that rapid proof-of-concept by providing frameworks that work reliably. Brownfield, Greenfield, and Everything Between Greg emphasized that open automation applies across implementation scenarios, not just new facilities. ExxonMobil’s Baton Rouge project represented greenfield application—full rip and replace using Open Process Automation (OPA) with Universal Automation Organization as a technology enabler. Kongsberg Maritime’s oil and gas platforms demonstrated brownfield application. They couldn’t replace everything on existing platforms. Instead, they added an orchestration layer using UAO technology on top of existing systems. Aimirim’s BAT deployment represented another brownfield approach—facilities with existing infrastructure where new capabilities deployed incrementally. The Orchestration Layer Strategy “Some people may tend to think that this technology is only for greenfield application,” Greg noted. “But actually, no. One of the easiest ways is going through the orchestration layer, adding a layer on top of what is existing.” Kongsberg Maritime standardized well barrier testing—previously done manually by different operators in different ways. They automated it using universal automation technology, orchestrating the process without replacing underlying systems. This flexibility matters because few organizations can afford complete system replacements. Furthermore, the ability to add open automation capabilities incrementally, proving value before expanding, enables adoption that wouldn’t happen otherwise. The Obsolescence Mitigation Strategy Dante highlighted a powerful advantage: “Creating that abstraction or control layer and being able to manage obsolescence simultaneously, developing your software simultaneously without having to worry about tremendous downtimes.” Traditional automation faces a harsh reality. When hardware reaches end-of-life, organizations face downtime, capital expenditure, and risk. With hardware/software decoupling, organizations abstract the control layer first, then gradually replace underlying hardware. “Building it into that layer over time as opposed to ripping everything out and starting over,” Dante summarized. Greg emphasized the investment perspective: “You need to ensure that this money which is invested, you will have your return on investment quite soon. But as well, that your investment lasts a certain amount of time.” The “This Is Happening Now” Moment Sree posed the critical question: “Could we say that the big takeaway here is we’ve been talking about this for a while, but this is happening now?” “This is happening now,” Greg confirmed emp

    32 min

  2. JAN 6

    Episode #16 | 2025 Year in Review: The Themes That Defined Digital Transformation

    As 2025 drew to a close, Episode 16 of Practitioners Unplugged took a reflective look back at the conversations that shaped our understanding of digital transformation throughout the year. Cohosts Dante Vaccaro and Sree Hameed unpacked the common threads, surprising insights, and memorable moments from a year of conversations with practitioners driving real change. What started as a podcast focused broadly on digital transformation evolved into something more nuanced—a collection of stories revealing the human challenges, infrastructure realities, and cultural shifts that determine whether technology initiatives succeed or fail. From live recordings at Automate in Detroit (complete with an infamous train that became a running joke) to conversations about change management and AI’s reality, 2025 brought clarity to what actually matters in Industry 4.0 and beyond. “It really seemed like it kind of morphed into little breadcrumbs from everyone we talked to kind of evolving into different drawdown topics that didn’t even think to consider,” Dante reflected on the year’s journey. A Year of Voices: The 2025 Guest Lineup The practitioners who made 2025’s conversations possible: Jonathan Wise, Chief Technology Architect at CESMII Brett Redmond, EcoStruxure Automation Expert at Schneider Electric Jim Davis, UCLA Vice Provost IT Emeritus CESMII Program Oversight & Principal Investigator Daniel Harr, CEO at Delta Systems & Automation Jim Mayer, Founder of The MFG Connector and Host of Manufacturing Culture Podcast Jason Head, Controls Development Engineer at Fallas Automation Jonathan Alexander, Manufacturing AI & Advanced Analytics Manager at Albemarle Intelligence Ed Koch, Chief Solutions Officer at CCi Craig Henry, Global Account Director for Amazon at Murrelektronik Nick Valdez, Automation & Controls Business Development Manager at Vessco Water Each brought unique perspectives from different corners of the industrial ecosystem, painting a comprehensive picture of where digital transformation stood in 2025. The Five Major Themes That Emerged in 2025 1. People Don’t Hate Change—They Hate Being Blindsided by It Jim Mayer delivered one of the year’s most memorable insights when he challenged a fundamental assumption about transformation initiatives. “Humans hate change, and that’s just not true,” Jim explained, “What did you have for lunch today, Sree?” Sree replied, “I had a slice of pizza.” Jim then turned to Dante, “What did you have for lunch today?” Dante replied, “Kebabs.” Jim concluded his point, “I had a wrap. We love change.” The real issue isn’t change itself—it’s how change is managed and communicated. When people feel blindsided, resistance follows naturally. However, when teams are brought along and feel like participants rather than victims, adoption becomes significantly easier. “Change has to be done simultaneously with the strategy, bringing people around from day one,” Dante observed. “It can’t just be the afterthought at the end where we say, hey, nobody’s doing what we set out to do.” The Center of Excellence Challenge Sree connected this to multi-site MES rollouts, noting the critical importance of centers of excellence that genuinely included stakeholders rather than dictated to them. When people participated in the design process, projects tended to progress much more smoothly. The lesson applied broadly: organizations needed to question whether they were truly bringing people along or simply informing them of decisions already made. 2. Culture Wasn’t a Soft Topic—It Was the Foundation for Technology Adoption Nick Valdez articulated a challenge many guests touched on when discussing water infrastructure modernization: “We struggle with people really understanding what information they need and what’s the best way to achieve that information because not everyone in the water wastewater industry is on the modernization train. They don’t necessarily want to spend the capital or invest the time or energy or effort.” Across industries, culture determined whether organizations could even recognize opportunities for improvement. When “that’s the way we’ve always done it” became the default response, technology investments delivered minimal returns. Jim Mayer’s work on manufacturing culture reinforced this point. His focus on creating environments where people wanted to work rather than simply needed to work addressed the human foundation that made everything else possible. Technology amplified existing culture. If the culture valued learning and improvement, technology accelerated both. If the culture resisted change, technology became another source of friction. 3. Training and Upskilling Weren’t Optional Anymore—They Were Strategic Imperatives The conversation kept returning to workforce challenges throughout 2025. Experienced workers were retiring with institutional knowledge, younger workers had different expectations about development, and technology evolution meant even veterans needed ongoing skill development. Organizations addressing this successfully shared common approaches: structured knowledge transfer programs, apprenticeship and mentorship models, continuous learning culture, and technology as a capability multiplier rather than replacement. “It does pay off even if it means projects get delayed in terms of being able to find the right resources,” Sree noted. “You do end up making change that is sustainable.” Rushing implementation with inadequate capability development created technical debt. Taking time to build capabilities properly produced more sustainable results. 4. Data Quality and Standardization Remained Fundamental Challenges While AI grabbed headlines throughout 2025, practitioners consistently emphasized that more fundamental data challenges continued to limit what organizations could actually accomplish with advanced technologies. Jonathan Wise’s discussions around Unified Namespace and Craig Henry’s “industrial nervous system” metaphor reinforced the same point: organizations investing heavily in AI (the “brain”) while neglecting connectivity and data infrastructure (the “nervous system”) created powerful capabilities that couldn’t sense what was happening on the factory floor. Organizations making progress addressed these systematically rather than expecting AI to magically overcome poor data foundations. They invested in connectivity, established data governance, and created infrastructure that enabled data to flow where it was needed when it was needed. 5. AI’s Promise Versus Reality—Closing the Gap Required Honesty If there was one topic that generated both excitement and skepticism in 2025, it was artificial intelligence. The year brought clarity about the gap between vision and reality. Sree captured the challenge: “I look at AI and my reaction is that is not in my comfort zone. I want explainability and transparency and see the promise of AI and I’m still okay, there’s a lot of learning to do here.” When AI systems functioned as “black boxes” without explaining reasoning, practitioners struggled to trust them for critical decisions. In manufacturing operations where safety, quality, and efficiency had direct consequences, this transparency gap mattered enormously. Practical AI Versus Generative Hype Dante made an important distinction: “When we talk about practical AI, this goes back into what’s proven. This is more machine learning. This is neural network building. All of this stuff existed before without the generative component to it on top.” Established forms of AI—machine learning for predictive maintenance, computer vision for quality control, optimization algorithms—had proven track records. These applications delivered measurable value in specific use cases. In contrast, generative AI represented something newer, less proven in industrial contexts. While the promise was significant, so were the unknowns around reliability and appropriate applications in high-stakes environments. The conversation pointed toward where 2026 needed to evolve: from hype toward practical implementation stories. What was working? What wasn’t? How could organizations maintain human expertise even as they deployed AI tools? The Industry 4.0 to Industry 5.0 Progression: No Skipping Steps One of the year’s clearest messages: organizations couldn’t skip foundational work. As a customer Sree referenced noted: “There is no Industry 4.0 without Industry 3.0. It builds on that, right?” Organizations rushing to embrace Industry 5.0 concepts without properly implementing Industry 4.0 foundations faced significant challenges. “If you didn’t start your Industry 4.0, you’re going to be left in the dust,” Dante emphasized. Successful modernization required honest assessment of current capabilities and systematic progression rather than attempting to leapfrog without building necessary foundations. Lessons from Live Recording: The Infamous Automate Train The live recording experiences from Automate in Detroit added memorable dimension to 2025’s podcast journey. Recording on the show floor captured real energy and context—along with an unexpected guest star: a train that repeatedly rolled through recordings, eventually becoming a running joke turned into a montage of train puns. “I didn’t realize actually how many times that train really just disrupted the flow of the conversation,” Dante laughed while reflecting on the experience. The incident illustrated something important: authenticity and real-world context mattered more than perfectly controlled conditions. Real implementation happened in messy, imperfect environments. Success came from adapting and learning rather than waiting for ideal circumstances that never arrived. Looking Ahead: The 2026 Research Agenda As 2025 concluded, Dante and

    49 min

  3. 12/04/2025

    Episode #15 | From Run-to-Failure to Predictive Operations: Transforming Water Infrastructure

    From Run-to-Failure to Predictive Operations: Transforming Water Infrastructure with Nick Valdez Most people take for granted that clean water flows when they turn on the faucet. Wastewater disappears when they flush. Episode 15 of Practitioners Unplugged pulls back the curtain on this critical infrastructure with Nick Valdez, Automation & Controls Business Development Manager at Vessco Water. He reveals how digital transformation is revolutionizing an industry that quietly serves every community. Operating across 41 states through a portfolio of acquired companies, Vessco Water represents the evolution from component supplier to comprehensive solutions provider. Nick’s journey—from culinary school dropout to manufacturing to leading automation initiatives for water/wastewater systems—provides unique perspective on bringing change to an industry historically resistant to modernization. This conversation explores a different dimension of Industry 4.0: not sexy manufacturing facilities, but the essential infrastructure that determines whether communities can grow, whether rivers stay clean, and whether taxpayers get value from their utility investments. As Nick puts it: “A lot of our customers don’t know what they don’t know. They don’t know that there are great products that can help them be smarter and better. They really lean on us as a platform to help them solve their problems.” Key Insights from Our Conversation Here are the five key insights from our conversation with Nick: 1. The Virtuous Cycle—Starting Small Creates Unstoppable Momentum Water and wastewater utilities face a classic chicken-and-egg problem. They won’t invest in sensors and data infrastructure until they see value. But they can’t see value without data. Nick’s team has mastered breaking this impasse by creating what he calls the “virtuous cycle.” (Originally “drinking the Kool-Aid” before our marketing expert Sree upgraded the terminology.) “Once they start, it continues, it perpetuates. It’s almost incredible to watch how customers really do drink the Kool-Aid and say, oh wow, this is incredible. We’re able to have this information, now we can use it, and then it makes their lives a lot easier.” How the Pattern Repeats Across municipalities, the pattern repeats consistently. Initial reluctance gives way to pilot projects. Pilot results then drive broader deployment. Suddenly, utilities that ran equipment from the 1970s until failure are calling to retrofit entire station networks. What’s the key? Demonstrating tangible benefits in the first installation. This includes energy reduction, better operational visibility, or predictive maintenance that prevents costly failures. Moreover, this insight challenges the “big bang” digital transformation approach. In water infrastructure, patience and proof points matter more than comprehensive strategies. One successful pump station with VFD controls and smart analytics generates more momentum than any presentation about Industry 4.0 possibilities. The challenge Nick identifies is clear: “We struggle with people really understanding what information they need and what’s the best way to achieve that information because not everyone in the water wastewater industry is on the modernization train.” 2. Regional Diversity Demands Flexible Solutions—41 States, 41 Different Mindsets Vessco Water’s geographic spread—41 states and growing—provides insight into how dramatically water infrastructure approaches vary across America. New York City faces completely different challenges and regulations than Iowa communities. This requires solutions that adapt while maintaining quality standards. “In New York City, our platform partners there have a different challenge and different mindset of their customer than say what one in Iowa does. The real trick to it all is for us as a platform to have the resources and the availability of equipment and engineering and manufacturing to make a holistic approach to solving each of the different mindsets of our customers.” The Pillar Strategy Consequently, this diversity drives Vessco Water’s pillar strategy: service, aftermarket, automation and controls, pumps, and distributed products. Rather than forcing one-size-fits-all solutions, they combine specialized capabilities from different regional partners to address local requirements. For example, an Iowa partner specializing in pump distribution might team with a regional automation expert to serve an engineering firm’s specific needs. The lesson extends beyond water infrastructure to any organization serving diverse markets. Standardization matters for capabilities and quality. But application requires flexibility. Build pillars of expertise that can be combined differently depending on customer context rather than rigid solutions that ignore regional differences. Nick’s point about becoming “subject matter experts” resonates here. Customers lean on Vessco Water precisely because they understand the nuances of different regulatory environments, population densities, and community expectations across geographies. 3. Relationships Trump Transactions—Equipment That Lasts Decades Requires Trust Unlike consumer goods or even most industrial equipment, water infrastructure investments span decades. This time horizon fundamentally changes how buying decisions happen and what matters to customers evaluating partners. “People buy from people in our industry. It’s very rare someone looks on the internet and just says, oh, I wanna buy a flight pump. They know the local community uses them. They call their local rep or distributor. They call one of our platform partners, come out and talk to me. Let’s build a relationship because the equipment that you’re gonna put in and the solution you’re gonna provide has to last for decades. That’s the expectation.” Building Trust Through Education This relationship focus shapes Vessco Water’s entire approach. They offer free training classes on drives and pump optimization. They provide arc flash studies and safety training. They bring mobile equipment demonstrations to municipalities and colleges. These investments build trust and educate customers who “don’t know what they don’t know” about available solutions. The strategy addresses a critical industry challenge: water infrastructure expertise is retiring without adequate knowledge transfer. Vessco Water’s “new leaders group” creates mentorship paths where experienced professionals transfer “ancestral knowledge” about handling specific situations and selecting appropriate equipment. The Broader Lesson In industries with long equipment lifecycles and high switching costs, customer education and relationship building deliver better returns than transactional selling. Your ability to help customers make informed decisions matters more than any individual product sale. 4. Run-to-Failure Is Expensive—Predictive Operations Deliver Multi-Dimensional Value Nick’s Minnesota success story illustrates how modernization transforms economics and community impact simultaneously. A municipality running pumps with “archaic” starting methods moved to variable frequency drives with smart analytics. This generated benefits across multiple dimensions. “They were able to reduce the amount of energy they were gonna have to use for their treatment of wastewater. They were able to support more people moving to that city. We could interpolate and predict high flow scenarios. So when it was gonna rain or when people were getting off work, we were gonna have more of an intake to the facility.” Multi-Dimensional Benefits Energy optimization reduced costs for taxpayers. Predictive capabilities prevented overflow events that would contaminate rivers and trigger regulatory fines. Better operational control enabled the municipality to support population growth. The technology investment paid dividends far beyond the initial business case. Nick emphasizes downstream effects people don’t consider: “If they don’t have equipment in place that can understand that and predict that, or recognize that scenario in real time and then augment and change to mitigate it, then they do have these events where maybe they flood a whole bunch of houses or there’s regulatory fines and penalties for them having to divert effluent into a creek or a river.” The parallel to manufacturing is clear: digital transformation delivers value across multiple dimensions simultaneously. Don’t evaluate investments purely on energy savings or maintenance reduction. Consider community impact, regulatory compliance, growth enablement, and risk mitigation together. 5. Culture and Quality Control Drive Insourcing Decisions—Brand Promise Matters More Than Cost While many manufacturers outsource to reduce costs, Vessco Water makes the opposite choice. Nick explains their decision to keep manufacturing, engineering, and quality control in-house despite seeing competitors find success with outsourcing. “If we outsource some of that work, the quality control measure gets limited. We want to control the quality that our customers get and the experience that they have so we can put our stamp and brand on it and we know for a fact what the outcome’s gonna be.” Brand Promise Over Cost Optimization This reflects a broader philosophy about customer experience versus short-term cost optimization. Vessco Water offers service agreements guaranteeing pump replacement every five years and 100% operational uptime. Delivering on these promises requires controlling the entire value chain. Furthermore, Nick’s passion for company culture is evident: “Everybody is important. Everybody has their role and responsibilities that contribute to it, and we all breathe and believe in the same thing as we have one common goal. The most beautiful thing about our com

    35 min

  4. 11/04/2025

    Episode #14 | The Industrial Nervous System: Why Your Factory's AI Brain Needs a Body with Craig Henry

    If you’ve invested in AI, machine learning, and digital twins only to see disappointing returns, Episode 14 of Practitioners Unplugged diagnoses the problem. Craig Henry, Global Account Director for Amazon at Murrelektronik and author of Super Connected: The Future of Industrial Nervous System, cuts through the AI hype with a sobering truth: your factory’s expanding “brain” is useless without a properly designed nervous system to connect it to reality. With over 30 years in advanced manufacturing and inter-logistic systems—including major roles at Amazon, Siemens, and Danaher—Craig brings a practical, infrastructure-focused perspective to Industry 4.0 and 5.0. His book and this conversation challenge the current obsession with AI capabilities by asking a more fundamental question: can your AI actually feel what’s happening on your factory floor? As Craig puts it: “We’re operating a brain that is suspended in space and isn’t connected to a body. Without the nervous system, we are crippled.” Here are the five key insights from our conversation with Craig: 1. The Forgotten Middle—Infrastructure Is Your Real Competitive Advantage While manufacturers pour resources into AI analytics platforms (the “brain”) and field devices (the “extremities”), Craig identifies a critical gap: the connectivity layer that bridges them. This “forgotten middle” represents the difference between AI that transforms operations and AI that generates impressive demos with no business impact. “As I’ve talked with Amazon and others in the field, I’m seeing that when you get the connectivity right, it opens up all of the promises of Industry 4.0 and Industry 5.0 and the digital twin. But without it we are crippled.” The problem is pervasive: manufacturers operate with 30-year-old control systems that create “black box effects.” A CEO cannot drill down through cybersecurity, edge infrastructure, and proprietary PLCs to see a single sensor reading that might explain why a critical customer order is delayed. The data exists but remains invisible. Craig’s prescription: invest in the forgotten middle before investing in AI. Move from analog signals to digital communication networks. Implement open architectures like OPC UA that expose data across the organization. This infrastructure investment delivers the fastest ROI because it enables you to see and feel what’s happening across your operations for the first time. The parallel to Amazon’s approach is instructive: Jeff Bezos mandated 20 years ago that every system would have an open API with exposed data. This infrastructure-first philosophy enabled Amazon’s rapid innovation and operational excellence—not because they had better AI, but because their AI could actually see the operation. 2. Digital Networks Beat Analog Signals—Even When Installation Costs $20,000 Craig addresses the objection every plant engineer faces: why spend $20,000 installing a $200 sensor when budget pressures demand “value engineering”? His answer reframes the investment from cost to insurance against catastrophic failure. “I would venture to say having that data could mean you’ve saved a million dollar problem down the line from happening. And the $20,000 would be small compared to what could be just operating blindly and hoping for the best.” The critical distinction: don’t install analog signals—install communication networks. Run digital, self-checking, deterministic, error-checking protocols through that expensive conduit run. This enables condition monitoring that moves unplanned downtime to planned maintenance, a transformation that pays for sensor investments many times over. Craig cites the harsh reality of “value engineering” in capital projects: data infrastructure gets cut first, then costs 30-50% more to retrofit later when problems emerge. Leading organizations take the opposite approach—they get data first, even before knowing exactly how it will be used, because they understand the infrastructure enables future capabilities. The lesson extends to OEMs and system integrators: companies that deliver equipment with comprehensive digital twins and exposed data differentiate themselves from competitors still selling hardware. The conversation shifts from price to ROI when you can demonstrate how your system will improve throughput by specific percentages. 3. Human in Command, Not Just in the Loop—Automation’s Critical Design Principle Craig challenges both the “lights out factory” dream and the passive “human in the loop” compromise with a more sophisticated paradigm: human in command. This distinction becomes critical as automation advances and skills fade threatens operational resilience. “It’s not human in the loop, but human in command. The system should be able to push up to him: I’m seeing this. Here are three options. Please choose one and approve it.” The analogy to chess masters illustrates the point: the highest performance comes from humans with computer assistance, not computers alone. The master can recognize patterns, apply judgment, and execute strategies that algorithms miss. Similarly, factory operators should manage 20 stations with AI flagging exceptions and recommending actions rather than pushing buttons on individual machines. This approach addresses multiple converging crises: the global labor shortage (500,000 positions unfilled in U.S. fulfillment alone), the competency crisis (87% of companies cite skill gaps as their biggest constraint), and the skills fade problem (automation that works well until catastrophic failure when humans have stopped understanding the underlying process). Craig’s warning about autonomous driving accidents applies equally to manufacturing: when humans assume automation has everything under control, disasters happen. The solution isn’t eliminating automation but designing systems where humans maintain operational understanding and decision authority even as AI handles routine optimization. 4. Open Standards Are Non-Negotiable—Proprietary Systems Are Ransom, Not Strategy Craig takes a strong stance on the standards debate, arguing that proprietary protocols and closed systems amount to holding customers hostage. For manufacturers evaluating equipment purchases or system upgrades, this represents a critical strategic decision. “If I decide, hey, I’m going to force my customers who buy my equipment to use my protocols and my networks to the exclusion of everybody else, what they’re really doing is holding their customer for ransom. And that’s not cool. It’s not okay.” His recommendation is clear: use OPC UA, which bridges devices to cloud, includes built-in encryption, and democratizes access across vendors. Avoid fieldbus systems controlled by single vendors. Demand that OEMs expose data through standard interfaces even if internal IP remains protected. The business case for open standards extends beyond technical elegance. Standards enable faster deployment, broader talent pools for implementation and support, and protection against vendor dependency. When Amazon and other large end-users mandate specific protocols, they’re protecting their ability to innovate independent of equipment vendor roadmaps. For OEMs, Craig’s advice flips the conventional wisdom: don’t view open standards as giving away competitive advantage. Instead, differentiate through consulting on process improvement and ROI rather than locking in through proprietary interfaces. Companies that help customers achieve measurable performance gains win business regardless of protocol choices. 5. Cybersecurity Is Everyone’s Problem—And Skills Fade Is Your Hidden Vulnerability Craig delivers sobering statistics: cybersecurity attacks now generate more revenue than illegal drug trafficking, with loss of use (ransomware) as the primary attack vector. For manufacturers, this isn’t just an IT problem—it’s an operational resilience issue that requires scenario planning and regular drills. “We have to be able to have plan A, plan B, plan C, and to your point that a human is there skilled and experienced enough to be able to drive that and to make those decisions.” The challenge intensifies with automation dependence. Craig’s example of his wife’s county clerk office illustrates the problem: when systems went down, resistance to manual processes was so strong that basic operations nearly stopped despite manual procedures being available. In manufacturing, returning to manual operation may be impossible when robots perform tasks no human workforce could replicate. The solution requires acknowledging that attacks will happen and preparing accordingly: secure data in three places, maintain operational knowledge that doesn’t depend on systems, and ensure humans understand underlying processes enough to make decisions during disruptions. This represents the flip side of the human-in-command principle—humans must maintain enough competency to take control when automation fails or becomes compromised. Craig’s perspective on large language models adds another dimension: 80% of people using LLMs can’t recall the content they produced. If we build similar dependency in manufacturing operations, how do humans respond when AI systems become corrupted? The answer requires intentional design to maintain human competency even as automation advances. Conclusion: Building the Body Your AI Brain Needs Craig Henry’s infrastructure-focused approach provides the reality check that digital transformation initiatives need. While the industry obsesses over AI capabilities—with projects like Stargate committing $400 billion to expand the “brain”—the nervous system that connects that brain to physical operations lags dangerously behind. The key lessons for practitioners: invest in the forgotten middle connectivity layer before chasing AI soluti

    1h 6m

  5. 10/02/2025

    Episode #13 | The "Way" of Manufacturing: Why Principles Should Guide Technology Roadmaps (And Not the Opposite) with Ed Koch of CCi

    If you’ve wondered why some manufacturing organizations consistently outperform their peers while others struggle to scale operational improvements, Episode 13 of Practitioners Unplugged provides the blueprint. Manufacturing excellence principles, not technology purchases, form the foundation of sustainable success. Ed Koch, operations veteran with over 20 years across Unilever, SAB Miller, and AB InBev, cuts through the digital transformation noise with a timeless truth: sustainable manufacturing excellence starts with building capabilities, not buying technology.” Working across six continents and 120+ facilities, Ed helped design and implement SAB Miller’s legendary “Manufacturing Way”—a systematic approach to operational excellence that enabled rapid integration of acquisitions while maintaining performance standards. His philosophy challenges the current obsession with AI and automation: focus on developing people and processes first, then leverage technology as an accelerator. As Ed puts it: “Manufacturing is a team sport, and excellence is only achieved through deliberate focus on continuous improvement and organizational learning.” Here are the five key insights from our conversation with Ed: 1. Build “Ways,” Not Processes—Principles Scale Across Cultures and Contexts The concept of organizational “ways” goes far beyond standard operating procedures. For SAB Miller, the Manufacturing Way served as one of eight strategic capabilities (alongside Marketing Way, HR Way, etc.) that provided a unified framework for a rapidly growing, acquisition-heavy business. “The idea behind ways was really a single credible point of reference in how we operate, how we run the business, how we believed was the best way to run our operations,” Ed explains. This wasn’t about creating rigid processes but establishing enduring principles that could adapt to local contexts across Latin America, Europe, Asia, and Africa. The genius of the approach lies in its structure: overarching principles that don’t change, organizational design that adapts to regional needs, competencies that build over time, and work practices that evolve with technology. Think of it as a four-layer framework where the foundation remains stable while the surface adapts to changing conditions. This principle-based approach enabled SAB Miller to integrate acquisitions seamlessly. New facilities could understand “how things work around here” through a common language and methodology, accelerating both operational performance and career development across a global organization. 2. Manufacturing as Core Competency Separates Leaders from Followers Ed’s observations working with manufacturing leaders worldwide reveal a stark divide: companies that treat manufacturing as a core competency versus those that view it as a cost center to optimize. The leaders consistently pull away from the pack because they understand that operational excellence creates sustainable competitive advantage. “We have some clients that have fantastic performance and brilliant factories. One of the things that we observe is that the leaders continue to lead and in fact get faster, get better, faster. They innovate faster and pull away from the pack.” These leading organizations share common traits: strong engaged leadership close to the workforce and factory floor, well-trained manufacturing technicians and operators, and what Ed describes as “a restlessness, a hunger to find new ways to perform better.” The implication for executives is clear: manufacturing excellence isn’t achieved through periodic improvement initiatives or technology deployments. It requires sustained investment in building organizational capabilities that compound over time. Companies that understand this create performance gaps that competitors struggle to close. 3. The Master Manufacturing Technician—Reimagining the Factory Floor Role When designing SAB Miller’s “brewery of the future,” Ed’s team asked a fundamental question: if we built a brewery with the latest processes and technology designed to operate for 30-40 years, what would a day in the life of an operator look like? The answer revealed the inadequacy of traditional job descriptions. Modern operators must run equipment, ensure quality, perform basic maintenance, track performance, develop team members, and learn new skills continuously. It’s a complex, critical role that directly impacts business performance. “These are the people that really add value to the product. These are the people that make the difference in the business,” Ed emphasizes. This led to the concept of the “master manufacturing technician”—a role that leverages all available capabilities to solve problems in real-time. The parallel to pop culture is apt: like the Mandalorian with advanced gear and capabilities, the future manufacturing worker operates within a system that amplifies their problem-solving abilities rather than replacing them. This represents a fundamental shift from Henry Ford’s industrial model toward human-technology collaboration. 4. Maturity Models Drive Performance Through Internal Benchmarking One of SAB Miller’s most powerful tools was a maturity model covering ten operational areas with defined stages of development. This created a common language across 120 facilities worldwide and enabled sophisticated internal benchmarking and knowledge transfer. “It allowed us to see how all those facilities were both performing in terms of a defined set of metrics, but also how mature they were in terms of their work practices,” Ed explains. The system drove shared learning where lagging facilities could learn from leaders, creating a virtuous cycle of improvement. The assessment process balanced self-evaluation with external calibration. Facilities conducted self-assessments twice yearly, with regional calibration covering one-third of operations annually. This three-year cycle ensured consistency while maintaining cost-effectiveness. The key insight: maturity models must evolve as capabilities advance. What represented leading practice five years ago becomes middle-of-the-road today. Organizations must recalibrate their standards to maintain competitive advantage and drive continuous improvement. 5. Technology Amplifies Fundamentals—It Doesn’t Replace Them While AI dominates current manufacturing conversations, Ed maintains focus on fundamental operational excellence. His approach to digital transformation reflects hard-won experience: establish solid operational foundations before adding technological complexity. “I think if we create that standard way of operating around the world, it also creates a common language of people being able to talk to common processes and standard methodologies. When you start thinking about digitizing and using digital tools, you want to make sure that the processes and systems that you have in place, the fundamental building blocks, are good ones.” Ed advocates for a hybrid approach combining digital insights with human problem-solving. While real-time data provides valuable performance information, gathering cross-functional teams around physical problem-solving boards often produces faster results than sophisticated digital tools. The key is viewing technology as an enabler for proven methodologies rather than a replacement. Organizations with strong operational foundations can leverage AI and advanced analytics more effectively because they understand the underlying processes and can interpret technological insights within proper context. Conclusion: Building Manufacturing Excellence That Endures Ed Koch’s experience across global manufacturing operations demonstrates that sustainable excellence comes from systematic capability building, not technology deployment. The Manufacturing Way approach—anchoring on enduring principles while adapting practices to local contexts—offers a proven framework for organizations seeking lasting competitive advantage. The lessons extend beyond manufacturing to any operation-intensive business: identify strategic capabilities that differentiate your organization, build systematic approaches to develop those capabilities, create measurement systems that drive internal benchmarking, and maintain leadership that stays close to front-line execution. As manufacturing faces pressure from shorter careers, frequent organizational changes, and technological disruption, Ed’s approach provides stability. By building capabilities that don’t depend on individual knowledge or specific technologies, organizations create resilience that survives workforce turnover and technological transitions. The Manufacturing Way isn’t about resisting change—it’s about building the organizational foundation that enables rapid adaptation to whatever challenges emerge next. Companies that invest in these capabilities first position themselves to leverage new technologies effectively while maintaining operational stability. Excellence is built one principle, one process, and one person at a time. But once established, it creates momentum that separates leaders from followers for decades. To submit a request for a new episode topic from Practitioners Unplugged, visit our contact page Thanks for reading. Don’t forget to subscribe to our weekly newsletter to get every new episode, blog article, and content offer sent directly to your inbox. Explore more about Schneider Electric & AVEVA

    1h 5m

  6. 09/03/2025

    Building a Data Highway for AI Success in Manufacturing with Jonathan Alexander of Albemarle Intelligence

    If you’ve been tracking the hype around AI in manufacturing, you’ve heard it all—pilot purgatory, proof-of-concepts that don’t scale, shiny tools that don’t move the P&L. Episode 12 of Practitioners Unplugged cuts through that fog with Jonathan Alexander, Global Manufacturing AI and Advanced Analytics Manager at Albemarle. His philosophy is refreshingly clear: start with operational excellence, use technology as an accelerator, and make context the backbone of everything. Working within Albemarle’s global manufacturing excellence organization, Jonathan brings over 15 years of experience turning digital transformation buzzwords into measurable results. His approach challenges conventional wisdom: instead of chasing the latest AI trends, focus on solving universal manufacturing problems through standardized, scalable systems. As Jonathan puts it: “Semantic models are king and context is king.” Here are the five key insights from our conversation with Jonathan: 1. Manufacturing First, Technology Second—Build Your Analytics Highway When asked where his function sits—OT or IT—Jonathan’s answer reveals Albemarle’s core philosophy: “I would consider myself neither OT or IT, I would consider myself manufacturing.” This manufacturing-first lens shapes everything: problem selection, architecture, and adoption strategies. Jonathan’s most powerful analogy centers on infrastructure investment. Just as President Eisenhower’s Interstate Highway System transformed American commerce by creating standardized routes, manufacturing organizations need an “analytics highway” before they can scale AI initiatives. “We had to say, well, how are we, what problems are we going to solve? And the traditional way of people doing digital transformation is they find a technology and then they go and apply it on a certain use case, and then they step back and take that technology and find another place for it.” Instead of technology-first thinking, Albemarle started with their biggest universal problem: process variability. They built a standardized infrastructure using PI System’s Asset Framework to contextualize 76,000+ instruments across six sites. This 300-page standardization document—admittedly “the least fun thing” Jonathan ever created—became the foundation that enabled rapid deployment of analytics at scale. The ROI parallel is striking: the Interstate Highway System generated $6 for every $1 invested over 70 years. Similarly, Albemarle’s infrastructure investment pays dividends through reusable templates that eliminate repetitive custom engineering work. 2. One Visualization to Rule the Chaos: Make Complex Analytics Look Like Google Maps Albemarle didn’t chase isolated use cases. They picked a global class of problems—process variability—and built a standard path from raw signals to action. Rather than building custom dashboards for each use case, they standardized on Statistical Process Control (SPC) charts as their universal interface. “We said, okay, imagine if we have all of our instruments monitored with this, with the right filtering so that they’re giving good signals. It’s kind of like the concept of Google Maps.” Just as Google Maps uses consistent visual language across any destination, Albemarle’s approach means operators in Australia, China, or South America see the same interface: green dots for normal operation, red dots for out-of-control conditions. This eliminates training barriers and enables consistent interpretation across diverse workforces. Think of SPC at scale as traffic conditions across thousands of “lanes” (tags): you can spot congestion instantly and know where to focus. It’s common-sense, teachable, and consistent—exactly what you need across regions, shifts, and skill levels. 3. Focus on “Action Boards,” Not Dashboards—Standard Templates Enable Speed Jonathan deliberately banned the term “dashboard” in favor of “action boards”—a semantic shift that fundamentally changed how his team approached analytics deployment. “You can create all these great insights, but if nobody used them, it’s just another art piece on the wall. And so for us, what we wanted to do is teach people not to be builders. We didn’t want all of our engineers to spend all of their time building new calculations and new dashboards.” The problem with traditional dashboards is that engineers love building them. Each creates their own version, then leaves the organization, forcing the next engineer to start over. This cycle wastes enormous resources on redundant development rather than value-generating activities. With SPC as the front door to action, Albemarle layered in anomaly detection using principal component analysis (PCA). Here, the magic is the template. Albemarle created standard unit-operation templates (with derived versions for reactors, columns, filters, etc.) tied into the PI Asset Framework. Once contextualized, “we could build a machine learning model and… have it deployed across hundreds of operators all in one hour.” They now run approximately 1,200 PCA models, typically trained on 2–12 months of relevant history for golden-batch-like behavior rather than strict predictive analytics. Standardization plus context equals speed. 4. Context Is King: The Bottleneck Isn’t Data Ingress—It’s Semantics A lot of manufacturers assume the historian is the heavy lift. For Albemarle, the harder part was building the semantic model over time and across generations of assets and drawings. “The challenge for us, it wasn’t getting data into the system, it was contextualizing it in the right way,” Jonathan explains. Automation of P&IDs and tag lists fell short because “not all of our P and IDs and our tag lists were accurate. They were done by different standards over a different time.” This contextualization challenge created unexpected resilience benefits. The true test of Albemarle’s system came during significant workforce turnover at one facility. Traditionally, losing multiple operators, engineers, and managers simultaneously would trigger weeks of downtime as institutional knowledge walked out the door. “We had this system in place and in the past when that would’ve happened, that would’ve been just unbelievably disruptive to the manufacturing sites. But we didn’t, they didn’t experience that.” The standardized analytics infrastructure maintained operational stability despite the knowledge drain. More importantly, it created a higher baseline performance level that doesn’t degrade when people leave. In a world of rotating contractors, generational change, and tight labor markets, this resilience is strategic. 5. Let Business Value Drive Technology Choices: GenAI’s Place (For Now) While generative AI dominates current conversations, Jonathan maintains focus on traditional machine learning techniques that deliver measurable ROI. His principle component analysis models have prevented costly equipment failures by detecting correlation changes that human operators miss. Albemarle is experimenting with genAI (like code acceleration in Microsoft Copilot), and the time savings can be stunning. But Jonathan is candid about the challenge: “The challenge that people are having with generative AI… is being able to specifically describe the business case and the ROI,” beyond soft savings. “I think with the advances in generative AI right now, semantic models are king and context is king. ‘Cause we’ve actually done some stuff with generative AI, just dumping a bunch of data from here into there and kind of crossing our fingers and praying that it will understand the information that we give it and it doesn’t.” As a result, genAI is “less than 10% of my time” today—while the team continues to scale SPC and machine-learning methods that tie directly to measurable process outcomes. Jonathan’s team uses generative AI where it adds clear value while continuing to expand traditional ML applications that directly impact manufacturing performance. This balanced approach avoids both “shiny object syndrome” and technological conservatism. Conclusion: Building the Foundation for Manufacturing’s AI Future Jonathan Alexander’s journey at Albemarle demonstrates that successful AI at scale requires patience, standardization, and relentless focus on manufacturing fundamentals. Their approach—treating AI as an enabler for proven continuous improvement methodologies rather than a revolutionary replacement—offers a practical roadmap for other manufacturers. The key lessons for practitioners: Anchor on variability and value through SPC at scale. Template everything to turn months into hours for model deployment. Invest in the semantic model because ingestion plumbing is necessary, but semantics is decisive. Design for turnover with standard visuals and thresholds. Keep genAI grounded with clear business cases tied to the semantic layer. Jonathan sums up the ethos well: don’t let “the AI tail wag the operations excellence dog.” Technology is “just an enabler” to move faster through proven continuous improvement methods. The lesson extends beyond AI to digital transformation broadly: sustainable change comes from solving universal problems through systematic approaches, not from deploying impressive technology demonstrations. Organizations that build their “analytics highway” first create the foundation for whatever technologies emerge next. The highway is built one mile at a time, but once complete, it transforms how quickly you can reach any destination.   To submit a request for a new episode topic from Practitioners Unplugged, visit our contact page Thanks for reading. Don’t forget to subscribe to our weekly newsletter to get every new episode, blog article, and content offer sent directly to your inbox. Explore more about Schne

    1h 11m

  7. 08/05/2025

    The Future of Packaging: Flexibility and Speed in Manufacturing with Jason Head of Fallas Automation

    In this episode of Practitioners Unplugged, we dive into the future of secondary packaging automation with Jason Head, Controls Development Engineer at Fallas Automation. Broadcasting from the floor of the Automate Show in Detroit, hosts Dante and Sree sit down with Jason to uncover how Fallas is addressing shifting demands in speed, flexibility, and data-driven design. Fallas Automation builds case packers and secondary packaging systems that sit between primary packaging and end-of-line palletizing — a critical but often overlooked link in the packaging value stream. Jason brings over a decade of hands-on experience in installation, troubleshooting, and now R&D, giving him a front-row seat to how digital transformation is reshaping OEM roles, customer expectations, and machine intelligence. Here are the five key takeaways from our conversation with Jason: 1. Data-Driven Demands Are Reshaping OEM-Customer Dynamics Gone are the days when a machine’s job was to “just run.” Today, end users demand data-rich systems capable of delivering performance metrics, predictive alerts, and operational visibility in real-time. Jason notes a dramatic uptick in customer requests for specific data structures, not just raw data points. Each enterprise seems to have its own “custom standard,” requiring OEMs like Fallas to build tailored data delivery mechanisms. This complexity can slow deployment timelines and increase engineering lift — especially when aligning with proprietary IT/OT frameworks on the customer side. “Each big corporation kind of has their own customized standard that we have to tailor our equipment to… and that definitely holds up getting machines out quickly.” The packaging equipment of tomorrow must be natively data-integrated, not just sensor-ready. 2. Predictive Maintenance Over Scheduled Downtime One of the biggest themes in manufacturing today is reducing unplanned downtime. While predictive maintenance has been a buzzword for years, Jason sees real traction emerging now — particularly in sensor-based component monitoring. Fallas is embedding smarter sensors that help detect part failures before they occur. By moving from scheduled downtime to condition-based maintenance, customers can avoid costly shutdowns and better plan maintenance cycles around actual usage patterns. “We want to incorporate sensors to detect component failures… so our customers can do predictive maintenance instead of run-it-till-it-breaks.” For manufacturers, that means longer machine life, higher uptime, and better OEE across the board. 3. Flexibility at the Operator Level is Critical With turnover rising and skill levels varying widely across facilities, OEMs must build machines that are easy to operate — no matter who’s on shift. Jason emphasizes the importance of designing systems that don’t require robotics expertise or specialized knowledge to run efficiently. “We want to make it where you don’t have to be a robotics expert to package snack foods.” This push toward human-centered HMI design and intuitive machine behavior is a direct response to the transient nature of today’s industrial workforce. The goal is clear: empower operators, reduce the learning curve, and standardize performance regardless of background. 4. Speed is the New Competitive Edge — But It’s Not Easy When one of Fallas’s largest customers asked for a 50% increase in line speed, Jason and his team were forced to rethink everything — from software logic to end-of-arm tooling. Meeting that ask wasn’t just about better hardware; it demanded smarter engineering and faster iteration. “They said, ‘We want to run 50% faster. Can you do it?’ Not only can you do it, but tell us when you’ll be able to deliver it.” The ask was extreme — but not unrealistic. Jason’s team delivered. It required months of planning, iterative tuning on the customer site, and a coordinated effort across upstream and downstream systems. The lesson? Speed and flexibility are no longer opposing forces. The next-gen OEM must excel at both. 5. Collaboration is Key: Don’t Build in a Vacuum In a standout moment at the end of the episode, Jason delivers a powerful reminder for engineers and manufacturers alike: “Don’t be an island.” Jason reflects on his tendency — common among engineers — to want to solve every problem independently. But he’s quick to point out that vendors are an untapped resource, especially when it comes to understanding broader market trends and unlocking creative solutions. “Vendors can be a great resource… they see market trends from a lot of different angles. So you can get a lot of insight about how to build a better machine.” Collaboration with customers, integrators, and technology providers isn’t optional anymore — it’s essential to staying competitive in a fast-evolving market. Conclusion: Building the OEM of the Future Jason’s insights offer a compelling snapshot of how OEMs are evolving from equipment builders to system partners. Fallas Automation’s future-ready mindset — one that blends data integration, intuitive design, rapid deployment, and collaborative innovation — is a model for the new industrial era. Whether it’s designing machines that adapt to new packaging formats, embedding remote diagnostics and AI vision systems, or building intuitive UIs for transient workforces, Jason and his team are tackling Industry 4.0 challenges in real time. The key message? The future belongs to fast-moving, flexible OEMs who put usability, connectivity, and collaboration at the core of their design philosophy. As Fallas Automation proves, it’s not just about packing more products — it’s about packing more intelligence, adaptability, and service into every machine. To submit a request for a new episode topic from Practitioners Unplugged, visit our contact page Thanks for reading. Don’t forget to subscribe to our weekly newsletter to get every new episode, blog article, and content offer sent directly to your inbox. Explore more about Schneider Electric & AVEVA

    31 min

  8. 07/01/2025

    From Machines to Minds: Embracing the Human Element in Manufacturing Innovation with Jim Mayer

    This paragraph is hidden using inline CSS. Don’t worry about deleting me. Here’s some helpful Info: CAN’T FIND A BOX TO PASTE THE VIDEO CONTENT? GO TO THE TOP RIGHT OF THE SCREEN AND OPEN “SCREEN OPTIONS,” AND SELECT FORMAT– UNCHECK IT OFF AND THEN ON AGAIN. UPDATE AND REFRESH THE PAGE. VOILA. FOLLOW PARAGRAPH STRUCTURE OF THIS ARTICLE TEMPLATE AND YOU’LL BE GOLDEN! In the latest episode of Practitioners Unplugged, recorded live from the bustling Automate show in Detroit, we cut through the noise of cutting-edge technology to spotlight a crucial, yet often overlooked, aspect of digital transformation: the human element. We’re joined by Jim Mayer, a passionate leader with 25 years of industry experience, who delves into the cultural side of change management in manufacturing. As Dante and Sree, our hosts, point out, while AI and robotics dominate the headlines at events like Automate, the true success or failure of digital initiatives often hinges on how people adapt and embrace new ways of working. Jim, a self-described “resource provider” who started his career in industrial distribution after a stint as a carpenter, found his calling in machine shops, appreciating the camaraderie and practical nature of the people there. His journey through various roles, including advocating for machine shops at the NTMA and ultimately launching his own company focusing on “the humans of the industry,” reflects a deep understanding of the challenges and opportunities at the intersection of technology and the workforce. He emphasizes that while asset performance management has been a focus for years, the “fuzzy” human side has often been avoided, despite people being considered a company’s most important asset. For manufacturing leaders grappling with implementing new technologies, Jim’s insights offer a vital roadmap for preparing their teams, fostering adoption, and sustaining long-term cultural change. Here are key takeaways from our conversation: 1. Technology Implementation: It’s About Needs, Not Just Newness A common pitfall in digital transformation is the top-down imposition of technology without employee input. Jim recounts countless instances where owners attend trade shows, purchase impressive technology, and then instruct employees to implement it, often without asking if it’s truly needed or if it’s the right solution. The result? Expensive equipment gathering dust under tarps for years. Jim’s passion for people stems from realizing that technology’s true power emerges only at the intersection of human needs and technological capability. Successful technology adoption happens when employees are involved in the selection process, often by bringing them to shows or even sending them in leadership’s stead. 2. Redefining the “Asset”: Measuring What Matters in Human Performance The financial world often views people as a cost item rather than an asset on the balance sheet. This leads to incongruencies in how human performance is measured. Jim points out that companies often measure employee turnover (when they leave) but not retention (when they stay), unlike how they measure the current ROI of machine tools. This reluctance to quantify “soft skills” like empathy, communication, or culture makes it scary for organizations accustomed to tangible metrics like machine depreciation or production output. However, Jim contends that the shift where AI impacts white-collar jobs, just as automation impacted blue-collar roles, is leveling the playing field, making the human aspect undeniable. 3. AI and Automation: Empowering People, Not Replacing Them A common fear among the workforce is that AI and automation will replace their jobs. Jim, however, offers a powerful counter-narrative: AI won’t replace people on shop floors; rather, people who learn how to use AI, automation, and digital tools will replace those who don’t. He draws a parallel to the introduction of CNC machines – those who adopted the new programming methods thrived, while others either adapted to manual work or left the industry. This shift means the nature of jobs changes, often leading to upskilling and a more engaging career path. From machinists to programmers to robotics engineers, manufacturing offers a clear career trajectory for those embracing advanced skills. 4. Cultivating a Culture of Authorship and Celebration Jim strongly asserts that “humans hate change” is a myth. People love change, but only when they author it – when they have a say, when it feels substantial, and when they can write what that change looks like. The 70% failure rate in digital transformation efforts often stems from owners buying technology without employee input. Successful change initiatives involve: Soliciting “Things That Suck” Lists: Encouraging operators to identify their daily frustrations fosters buy-in and ensures solutions address real problems. “Shop Floor Shark Tank”: Jim implements a program where employee teams identify challenges, propose solutions, and leadership commits to implementing the best within 30 days. This empowers employees and builds a sense of ownership. Leading with Empathy: Leaders must communicate how new technology enhances roles, rather than replaces them. This human-centric approach fosters trust and collaboration. Celebrating Wins (Big and Small): Adopting an “Agile” mentality from the software world, manufacturers should celebrate every win, even small incremental ones. This sustains motivation and fosters a culture where mistakes are seen as learning opportunities, not reasons for punishment. 5. Aligning KPIs and Embracing Continuous Improvement For digital transformation to stick, KPIs must be aligned across the entire organization. If a machinist is measured on scrap rate, the owner should also be measured on scrap rate; if the floor is measured on OEE, the whole organization is. This fosters a “team win or team loss” mentality, connecting individual efforts to overall organizational success. Resistance to new, often lower, OEE metrics from automated systems can be overcome if performance management is a continuous process of regular conversations, not a surprising event. This consistent, small-scale effort leads to extraordinary long-term results, fostering a culture of accountability and continuous improvement. Conclusion: The Collaborative, Empathetic Path Forward Jim Mayer’s powerful message is clear: while technology accelerates our evolution, the future of manufacturing hinges on human-centered leadership and a proactive approach to change management. Success in digital transformation isn’t an afterthought; it must be at the forefront of strategy. By leading with empathy, inviting employees to co-author change, and fostering a culture of continuous learning and celebration, manufacturers can build resilient, innovative operations. Jim emphasizes that while change is never easy, it becomes significantly smoother when the entire team is engaged and supportive, rather than resistant. His insights underscore that the most advanced technologies will only reach their full potential when rooted in a strong, adaptable human culture. To submit a request for a new episode topic from Practitioners Unplugged, visit our contact page Thanks for reading. Don’t forget to subscribe to our weekly newsletter to get every new episode, blog article, and content offer sent directly to your inbox. Explore more about Schneider Electric & AVEVA

    46 min
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About

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.

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