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.

The practitioners who made 2025’s conversations possible:

Each brought unique perspectives from different corners of the industrial ecosystem, painting a comprehensive picture of where digital transformation stood in 2025.

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.”

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.

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.

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.

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.

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.

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?

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.

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.

As 2025 concluded, Dante and Sree outlined what they hoped to explore in 2026: more stories, more specifics, more honest accounts of what was working and what wasn’t.

“Stories, successes, trials, tribulations, what’s working, what’s not,” Dante outlined. “Learning the biggest impacts, the biggest failures.”

The emphasis on practical AI over hype would continue. The focus on people and culture alongside technology would remain central. And the commitment to learning from practitioners actually implementing initiatives rather than consultants selling visions would define the podcast’s approach.

If 2025’s conversations proved anything, it was that successful digital transformation required integration across multiple dimensions simultaneously. The best technology failed without capable, engaged people. Brilliant strategies couldn’t overcome cultures that resisted change. Big visions meant nothing without foundational infrastructure to support them.

Organizations that fragmented these elements—treating culture as HR’s job, technology as IT’s responsibility, strategy as leadership’s domain—struggled to achieve transformation that stuck. Those that recognized these elements must evolve together created conditions where change could take root and grow.

Dante and Sree expressed appreciation for their sponsors—Schneider Electric and AVEVA—whose support made the podcast possible while allowing editorial freedom.

Most importantly, they thanked listeners who showed up, shared episodes, and provided feedback throughout the year. The invitation remained open: if there were guests who should be featured, topics that needed exploration, or perspectives missing, the Practitioners Unplugged team wanted to hear about it. The community shaped the content, and that collaborative approach aligned with the podcast’s philosophy that the best insights came from practitioners sharing real experiences.

Looking back at 2025, Practitioners Unplugged provided something increasingly rare in discussions about digital transformation: grounding in reality.

The practitioners featured throughout the year offered nuanced perspectives. Yes, technology enabled new possibilities. But also: people and culture mattered more than most discussions acknowledged. Infrastructure determined whether advanced capabilities could actually function. Sustainable change required bringing teams along. And honest assessment of what was working versus hype helped organizations invest wisely.

As manufacturing and industrial operations moved deeper into Industry 4.0 and began exploring Industry 5.0 concepts, these grounded perspectives became increasingly valuable. Organizations that would thrive wouldn’t necessarily be those with the biggest technology budgets. Rather, they would be the ones that systematically built capabilities, developed their people, strengthened their culture, and deployed technology thoughtfully.

Practitioners featured in 2025 understood this. Their stories, struggles, and successes provided the practical wisdom that helped other organizations navigate similar journeys. That was the value Practitioners Unplugged delivered—not abstract theories or consultant frameworks, but lived experience from people doing the work.

Here’s to another year of conversations that cut through hype, celebrate honest problem-solving, and build community among practitioners driving real change.

Keep practicing. Keep learning. Keep transforming.

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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.”

Here are the five key insights from our conversation with Nick:

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.”

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.”

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.”

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.

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.”

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.

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.

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.”

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.

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.”

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 company is our culture.”

In addition, the insourcing decision also addresses talent challenges. Rather than struggling to find experienced engineers in a tight market, Vessco Water invests in developing people through mentorship, cross-platform learning boot camps, and technology that amplifies individual capabilities.

The strategic lesson: outsourcing and cost reduction aren’t always optimal strategies. When brand reputation and customer experience drive competitive advantage, controlling quality through vertical integration may deliver better long-term value despite higher upfront costs.

Nick Valdez’s perspective reveals that digital transformation reaches far beyond traditional manufacturing into infrastructure that touches every home and business daily. Water and wastewater systems represent critical infrastructure where modernization delivers community benefits that extend well beyond operational efficiency.

First, start small to create momentum through demonstrated value. Build flexible solutions that adapt to regional requirements while maintaining quality standards. Invest in relationships and education because equipment lifecycles demand trust. Evaluate modernization through multiple value dimensions including community impact. Consider whether vertical integration better serves brand promises than cost optimization.

As Nick advises those hesitant about modernization: “Pick up the phone and call somebody. Reach out, go to your local distributor, go to your local channel partner. Learn what’s out there. Really give yourself the opportunity to understand that there are incredible products and technology that can reduce the amount of headache and heartburn. Be proactive, not reactive. The more proactive you are, the less reactive you have to be.”

The water infrastructure story provides valuable perspective for all digital transformation initiatives. Technology alone doesn’t create value. Technology combined with trusted partnerships, customer education, and commitment to quality experiences drives lasting change.

While manufacturing often dominates Industry 4.0 conversations, infrastructure modernization demonstrates how these same principles apply to essential services that most people never think about until something goes wrong. The municipalities transforming from run-to-failure to predictive operations create value that reaches every constituent, even if that value remains invisible when everything works properly.

The ultimate measure of success isn’t impressive technology demonstrations but communities where clean water flows reliably, wastewater disappears safely, and infrastructure adapts to support growth while protecting the environment. That requires exactly the combination Nick describes: smart technology, trusted relationships, and commitment to customer experience over transactions.

While people may take clean water for granted, the professionals modernizing infrastructure understand that reliability requires constant innovation and partnership. The transformation from archaic pump stations to predictive operations happens one relationship, one pilot project, and one virtuous cycle at a time.

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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:

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.

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.

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.

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.

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.

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 solutions, move from analog signals to digital communication networks throughout your operations, design for humans in command rather than lights-out automation, demand open standards that prevent vendor lock-in, and prepare for disruption through scenario planning and competency maintenance.

As Craig warns, “We have an uninformed brain floating in space, thinking it understands things.” The physical world of manufacturing requires more than thinking—it requires feeling, sensing, and responding through a properly designed nervous system.

The path forward isn’t abandoning AI but building the infrastructure foundation that makes AI useful. Organizations that invest in super-connected operations—with digital networks exposing real-time data, open standards enabling scalability, and humans maintaining command authority—position themselves to leverage whatever technologies emerge next.

The revolution isn’t in the AI capabilities themselves but in the infrastructure that lets those capabilities touch reality. Build the nervous system first, and the brain’s potential becomes achievable. Skip this step, and your AI investments remain impressive demonstrations disconnected from operational impact.

The question isn’t whether to invest in digital transformation but where to invest first. Craig’s answer is clear: start with the body your AI brain needs to function.

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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:

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.

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.

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.

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.

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.

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.

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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:

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.

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.

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.

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.

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.

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.

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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:

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.

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.

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.

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.

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.

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.

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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:

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.

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.

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.

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:

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.

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.

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In the latest episode of Practitioners Unplugged, we took our microphones live to the vibrant Automate show in Detroit. This special setting offered a unique backdrop for our in-depth conversation with Daniel Harr, CEO of Delta Systems. With over two decades of deep industry experience, Daniel offers a pragmatic and insightful look at how custom automated solutions, especially for the demanding food industry, are undergoing a profound evolution. He cuts through the hype, revealing how a blend of necessity, technological advancement, and a strategic embrace of data is not just changing, but fundamentally reshaping the landscape of modern manufacturing.

At its core, Daniel’s insights center on the imperative for digital transformation to drive both efficiency and resilience in industrial operations. He unpacks how the recent pandemic dramatically accelerated the push for automation, transforming it from a mere competitive edge into a critical survival mechanism for many businesses. This was particularly true in the face of widespread labor shortages and rapidly shifting consumer demands. This new era of manufacturing demands a holistic view, requiring seamless integration of systems, valuable data, and a flexible, forward-thinking mindset at every level of an organization. Daniel emphasizes that success now hinges on adapting quickly and intelligently to an ever-changing environment.

For operations leaders, plant managers, and technology strategists navigating this increasingly complex landscape, Daniel’s conversation provides several critical takeaways, offering both challenges and actionable opportunities:

The COVID-19 pandemic served as far more than just a global health crisis; it was a powerful and unexpected accelerator for automation across the industrial spectrum. Manufacturers, particularly those in the essential food sector, swiftly moved from merely considering automation as a long-term investment to recognizing it as an immediate operational necessity. Daniel highlights how Delta Systems found itself directly instrumental in helping food manufacturers maintain crucial supply lines even as they grappled with severe labor force challenges. This period unequivocally demonstrated a clear trend towards machinery that requires significantly less manual intervention and increasingly leverages real-time data for unprecedented levels of optimization. The drive for automation wasn’t just about cutting costs; it was about ensuring continuity and resilience when traditional labor models faltered.

Daniel draws an important distinction between what he calls “intelligent machines” and the emerging frontier of truly “smart” or autonomous systems. He explains that intelligent machines, while highly sophisticated, largely operate based on high-end controls and meticulous pre-programmed logic. They execute tasks with remarkable speed and precision, maintaining efficiency by consistently normalizing processes and reacting according to their coded parameters.

The true leap, however, lies in the development and adoption of autonomous machines, where Artificial Intelligence (AI) plays a pivotal, transformative role. These advanced systems will possess the capability to analyze vast amounts of live data, make predictive adjustments on the fly, and even self-correct without human oversight. This revolutionary shift profoundly reduces the need for constant human intervention in repetitive or easily automated decision-making processes. The ultimate goal, Daniel asserts, isn’t to eliminate the human element from the factory floor. Instead, it’s about elevating the role of human workers to more high-value, complex problem-solving, and innovative tasks. This transition represents a significant cultural shift towards systems that can largely manage themselves, thereby freeing human operators to focus on strategic engagement and continuous improvement.

Traditional industrial automation has historically prioritized sheer efficiency and maximum throughput. However, Daniel argues that today’s rapidly evolving market demands a critical new imperative: agility. Consumer preferences shift quickly, product lifecycles shorten, and seasonal demands intensify, all of which necessitate automation systems designed for inherent adaptability.

Daniel stresses that building flexibility into automation systems right from the initial design phase is not just beneficial, but crucial, and far more cost-effective than attempting complex retrofits later on. This foresight allows manufacturers to seamlessly adapt to diverse materials, accommodate product variations, and respond to emerging market trends without requiring complete line overhauls. He references a strategic concept gaining traction among large Consumer Packaged Goods (CPG) companies: the “launch factory” model. These are highly agile operations, often outsourced to specialized co-packers, designed for rapid testing and introduction of new products. This distinct operational model enables swift market experimentation before successful items are brought in-house to highly optimized “cost factories.” This strategic separation allows companies to innovate aggressively without compromising their core operational efficiency.

Effective system interoperability and intelligent data utilization are no longer luxuries; they are paramount to sustained success in modern manufacturing. Daniel explains how the seamless flow of data between disparate systems enables crucial functions like predictive maintenance, significantly reduces costly downtime, and ultimately boosts overall operational efficiency. This critical capability has also spurred the escalating need for robust remote support and comprehensive lifecycle services. These offerings allow Original Equipment Manufacturers (OEMs) like Delta Systems to provide immediate, highly efficient assistance and ensure continuous, optimized operation for their clients, often preventing issues before they arise and dramatically reducing the need for costly on-site visits.

Daniel insightfully explores the critical topic of the manufacturing workforce. He posits that the factory workforce isn’t shrinking; instead, it’s undergoing a profound and necessary transformation. As operations become increasingly digital and automated, the demand shifts away from purely manual labor towards a highly skilled workforce adept at interacting with sophisticated digital systems. Training initiatives are evolving, moving beyond basic computer literacy to teaching the intricate nuances of complex industrial processes and advanced data interpretation. Daniel envisions a future where factory jobs are characterized more by intellectual engagement, strategic problem-solving, and technology management rather than traditional physical labor. This evolution, propelled by ongoing technological advancements and generational shifts in skill sets, is actively creating higher-quality, more engaging roles, ultimately helping to bridge the persistent labor gap that has challenged the industry for years.

Daniel Harr’s perspective is unequivocally clear: the future of manufacturing is not a solitary journey for any single company or technology. It is, at its heart, a collaborative endeavor. By fostering strong, strategic partnerships across companies, between diverse technologies, and within industries, manufacturers can collectively and strategically leverage innovation to stay not just competitive, but truly ahead of the curve. This collaborative approach ensures that industrial operations are not only highly efficient and resilient in the face of disruption, but also proactively prepared for whatever complex challenges and exciting opportunities the future industrial landscape presents. His insights from Automate offer an invaluable roadmap for anyone aiming to thrive in the rapidly evolving and increasingly interconnected world of Industry 4.0.

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Smart Manufacturing is often viewed through the lens of cutting-edge technologies—AI, automation, sensors, digital twins. But in Episode #8 of Practitioners Unplugged, Jim Davis, one of the original pioneers of the Smart Manufacturing movement, urges us to look deeper. With over four decades bridging academia, industry, and innovation leadership, Jim makes a compelling case that success in Smart Manufacturing isn’t just about adopting new technologies—it’s about preparing your data, aligning your strategy, and collaborating across ecosystems.

Many companies begin their transformation with the assumption that more tech equals more progress. But what Jim outlines is a fundamental distinction between digitization (converting analog to digital) and digitalization (rethinking operations with digital tools). Without understanding that difference, organizations risk stalling after the first wave of investment. True Smart Manufacturing begins when leaders shift their focus from adding technology to unlocking the value of data and breaking down silos between teams, departments, and partners.

For operations leaders, embracing Smart Manufacturing means cultivating readiness—both organizational and technological. Based on Jim’s insights, here are five key actions leaders can take to build a foundation that lasts:

As Jim reminds us, the real unlock isn’t just adding more tech—it’s using what you already have smarter. When you invest in your people, prepare your data, and design for collaboration, the benefits are transformative: better performance, greater agility, and global competitiveness.

Whether you’re a plant manager, digital transformation leader, or someone just beginning to explore Industry 4.0, this episode is packed with practical advice that cuts through the hype and focuses on what truly moves the needle.

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The concept of interoperability has gained traction in the industrial automation sector, particularly with the emergence of initiatives like Universal Automation. This initiative seeks to create a software ecosystem that fosters collaboration among different vendors, enabling a more integrated approach to automation. As highlighted by Brett Redmond, a seasoned professional in the industrial automation industry, the vision of Universal Automation revolves around establishing a shared open ecosystem based on a shared source runtime. This initiative aims to address the historical challenges of standardization across vendors, which have often led to implementation discrepancies and interoperability issues.

For operations leaders, the journey toward embracing interoperability begins with a mindset shift. It is crucial to recognize that interoperability is not just a technical requirement but a strategic advantage. To facilitate this shift, operations leaders can take several proactive steps:

Assess Current Systems and Processes: Operations leaders should conduct a comprehensive assessment of their existing systems and processes to identify areas where interoperability is lacking. This assessment should encompass not only the technologies in use but also the workflows that rely on these systems. Understanding the current landscape will provide a clear picture of the challenges and opportunities for improvement.

Foster a Culture of Collaboration: Interoperability thrives in environments where collaboration is encouraged. Operations leaders should promote a culture that values open communication and teamwork among different departments and teams. By breaking down silos and fostering collaboration, organizations can create an ecosystem where information flows freely, enabling more effective decision-making and problem-solving.

Invest in Training and Development: To fully leverage the benefits of interoperability, organizations must invest in training and development for their workforce. Employees should be equipped with the skills and knowledge needed to navigate and integrate various systems effectively. This investment not only enhances individual capabilities but also contributes to a more agile and responsive organization.

Engage with Technology Providers: Operations leaders should actively engage with technology providers to understand their interoperability capabilities. By collaborating with vendors who prioritize open standards and shared source solutions, organizations can ensure that their technology investments align with their interoperability goals. This collaboration can also provide insights into best practices and emerging trends in the industry.

Embrace Continuous Improvement: The journey toward interoperability is not a one-time effort but an ongoing process. Operations leaders should cultivate a mindset of continuous improvement, regularly evaluating and refining their systems and processes to enhance interoperability. This commitment to improvement will enable organizations to adapt to changing market conditions and technological advancements.

While the challenges of achieving interoperability may seem daunting, the potential rewards are substantial. By embracing interoperability, organizations can enhance operational efficiency, reduce costs, and improve overall performance. As Brett noted, the historical attempts at standardization have often fallen short due to varying implementation strategies among vendors. However, with initiatives like Universal Automation paving the way for a more cohesive ecosystem, the time is ripe for operations leaders to take decisive action.
 

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