“Standards and rigor be damned—that’s the cost of doing business.”

For three decades, this mentality defined manufacturing operations. Plant floor heroes solved problems with whatever tools they could cobble together. Innovation happened one use case at a time. Each solution worked brilliantly. Together, they created a technical debt crisis where a small manufacturer would need to spend $100,000 for capabilities they could now access for $4,000.

Episode 18 of Practitioners Unplugged, recorded live at Schneider Electric’s Innovation Summit, explored how that dramatic cost reduction became possible. John Dyck, CEO of CESMII (Clean Energy Smart Manufacturing Innovation Institute), explained why interoperability represents an inflection point for American manufacturing—particularly for the 98.4% of manufacturers that are small and medium-sized operations.

Manufacturing productivity in the United States declined for the first time in recorded history over the last five years. Meanwhile, AI at scale remains out of reach for most organizations, blocked by technical debt accumulated through decades of problem-solving.

“Interoperability is one of those loaded terms, but it remains a super important idea and capability with very real outcomes,” John acknowledged.

At its core, interoperability is the capability of disparate software systems to communicate seamlessly using standard interfaces and APIs. However, the definition matters less than the problem it solves.

Thirty years of manufacturing data solutions created significant technical debt. Great companies built highly innovative solutions for productivity, quality, and maintenance. Nevertheless, each one became its own data silo with its own stovepipe architecture.

“When great new capabilities show up, as we’ve seen with the fourth industrial revolution and certainly now with AI, we’re prohibited from innovating at scale because of that lack of rigor and lack of focus on data standardization,” John explained.

John posed a striking challenge: “I can count on one hand how many Fortune 100 companies have effectively scaled large-scale manufacturing operations platforms across their entire fleet of sites.”

The most resourced organizations still struggle to scale digital solutions. Not because the solutions don’t work—they work brilliantly. Rather, the lack of interoperability makes replication prohibitively complex and expensive.

This scaling failure devastates the 98.4% of US manufacturers that are small and medium-sized operations. These companies need access to smart manufacturing capabilities but can’t afford the $100,000+ price tags that come with traditional approaches.

CESMII’s approach centers on three foundational imperatives.

First: Standardized Information Models. Today, you’re building data context from scratch every single time, and there’s no way to share these models because the structure varies from vendor to vendor.

CESMII’s solution? Crowdsource standardized information models and make them freely available. “We can collaborate, come together as a community and build these standardized information models, put them in a marketplace, and make them accessible for every platform, for every manufacturer, for free,” John explained.

These models cover every manufacturing asset—pumps, conveyors, compressors, CNCs, robots. Instead of reinventing the wheel, manufacturers access pre-built, standardized models that work across platforms.

Second: Type-Safe Information Models. CESMII treats these models as “type safe”—preventing anyone from modifying the standard. This ensures consistency across deployments.

Third: Standard APIs for Universal Access. The third imperative creates an open standard API that allows any platform to expose those information models and allows any application to discover those objects and their relationships.

A small manufacturing company in Medina, Ohio with fewer than 20 employees faced major quality and reliability challenges. They couldn’t afford sophisticated software solutions or systems integration consulting.

Through a small systems integrator familiar with CESMII’s approach, they deployed a standards-based solution for less than $4,000.

“They were able to take a standards-based information model for a waste scale, bring that information into the cloud with no on-premise infrastructure and using Excel, solve their quality and reliability challenges,” John explained.

In a traditional environment? That same capability would have cost approximately $100,000.

This small manufacturer gained capabilities they could actually sustain. They didn’t have IT staff to maintain infrastructure or in-house expertise to connect complex systems. Nevertheless, with crowdsourced, open approaches, they now have a data platform that works with Excel, Power BI, or any other solution against the same API.

Sree raised a critical technical point: the limitation of hierarchical data models.

“I’ve never seen a manufacturing system that isn’t hierarchical,” John acknowledged. The ISA-95 model taught the industry to think in hierarchies: enterprise, site, area, line, asset.

“But when you bring material or people or energy into that process, the hierarchical model falls down,” John explained.

This limitation becomes critical when deploying AI at scale. “AI at its core wants to infer relationships—how equipment and operators and material interact,” John explained. “But in a hierarchical system that’s just not coded unless it’s custom coded every single time.”

CESMII’s answer? Graph relational databases—the same technology social media companies use to determine relationships. “That’s really powerful in manufacturing because now you can expose data and figure out causation and correlation you wouldn’t have dreamed of before,” Dante noted.

Graph databases treat every object as nodes with relationships to other nodes. Moreover, these relationships change over time. “AI can now infer real value from these relationships in ways that can be scaled very rapidly from site to site,” John explained.

Sree offered a powerful analogy for communicating interoperability’s value to leadership: “If every country comes to the UN with different languages, you need translators. But that’s exactly what we have—layers and layers of translators at different levels of the stack. When we say technical debt, that’s exactly what it is.”

These translation layers compound complexity and cost. Worse, they erode data trust. “When you go through layers of translation and all these Frankenstein integrations, you lose trust in the data,” Sree noted. This insight particularly resonated from his life sciences experience, where regulatory compliance demands absolute data integrity.

For CEOs, this creates the “aha moment.” Technical debt isn’t an IT problem—it’s layers of expensive translation that slow innovation, increase costs, and undermine confidence in the very data needed to make better decisions.

Sree observed that “the mindset challenge is bigger than the actual technical viability.” John agreed emphatically.

A significant part of the challenge involves helping manufacturers understand the disconnect between their business strategy and their digital or smart manufacturing strategy. “It turns out most manufacturers don’t have a cohesive digital or smart manufacturing strategy,” John revealed. When that epiphany happens, the rest falls into place.

John highlighted a fundamental challenge: “As Americans, we’re as individualistic as corporations as we are as people—we don’t like collaborating.” CESMII exists precisely to overcome this tendency. As a vendor-agnostic consortia, they bring competitors to the table to solve problems collaboratively. “One of the most important things we can instill is a mindset biased towards collaboration and partnering,” John emphasized.

This isn’t just philosophical. Interoperability cannot be solved by any one vendor or any one manufacturer alone. Therefore, it demands the kind of collaborative effort that CESMII facilitates.

John described what he called the “bailing twine and duct tape mentality” that created current technical debt. “Fred or Sue on the shop floor—she’s gotta solve that problem right now so we can get product out the door. Standards and rigor be damned, that’s the cost of doing business.”

This problem-solving approach made heroes of individuals who kept operations running. However, scaled across 30 years and thousands of sites, it created the interoperability crisis manufacturers face today. The transformation requires moving from heroic individual problem-solving to systematic, scalable solutions.

“We’re at an inflection point,” John emphasized. “I’m very aggressive in telling every manufacturer I have the opportunity to speak with: You can continue propagating this technical debt, this cost and complexity, or you can choose a different way—a way that will lead you towards productivity, competitiveness, fundamentally enabled by interoperability.”

Manufacturing productivity in the United States has declined for the first time in recorded history. The next 30 years cannot be marked by the same stovepipe architecture approach that defined the last 30. Consequently, manufacturers face a choice: lead the transformation or scramble to catch up later.

CESMII’s strategy centers on three pillars: knowledge (training and certification programs), technology (the three Smart Manufacturing Imperatives), and ecosystem collaboration. “All three have to be a vital part of any transformation,” John emphasized.

For American manufacturing to reverse its productivity decline, capture AI’s potential at scale, and remain globally competitive, more organizations need to choose a different way. The inflection point is here. The question is whether manufacturers will recognize it and act while they still lead rather than follow.

Ready to explore interoperability? Visit CESMII.org to learn about knowledge programs, technology initiatives, and how to join the collaborative ecosystem transforming smart manufacturing.

Keep practicing. Keep learning. Keep transforming.

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