Insights from E Tech Group’s Matt Wise and Cole Switzer on scaling digital twins, building the data backbone and turning simulation into measurable ROI.
For years, digital twins have been discussed as a promising, but often abstract, technology for manufacturers. Today, that promise is quickly becoming reality. As adoption accelerates across industries such as life sciences, food and beverage, consumer packaged goods (CPG), energy and data centers, digital twins are emerging as a practical tool to reduce risk, improve performance and prepare operations for the next wave of digital transformation.
On a recent episode of the Ctrl+Alt+Mfg podcast, Matt Wise, CEO of E Tech Group, and Cole Switzer, principal full stack developer and head of the company’s advanced software division, shared how digital twins are being deployed in real manufacturing environments and why they are gaining momentum now.
What a digital twin really is and why definitions are evolving
While the term “digital twin” is widely used, its meaning has expanded beyond a single definition. According to Switzer, the most important shift is understanding that a digital twin is not just a visual model of equipment, but a representation of the underlying data and processes that drive a business.
“At its core, a digital twin is the replication of the underlying data and processes that make up a company,” Switzer said. “Whether you’re moving boxes on conveyors or managing biological processes in a life sciences facility, the goal is the same: Visualize, anticipate and analyze what’s happening before you touch the real system.”
Switzer described three broad categories of digital twins now being used in manufacturing. Simulation twins allow engineers to test ideas and system designs without physical equipment. Data twins focus on computational models that replicate how a process behaves. Visual or live twins combine real-time data with 3D environments to show how machines and systems are performing as operations run.
This flexibility is one reason digital twins are now applicable far beyond discrete manufacturing.
“Life sciences facilities don’t have a lot of moving parts, but they still need digital twins,” Switzer said. “The difference is what you’re modeling — data and biology instead of conveyors and robots.”
Why digital twin adoption is accelerating
Digital twins are not a new concept, but Wise noted that cost, complexity and unclear business cases once limited their use. That has changed.
“Five or 10 years ago, digital twins were expensive and often built just because they looked impressive,” Wise said. “Now we’re seeing very clear use cases with measurable ROI, and payoff windows that are surprisingly short.”
Those returns often come from avoiding downtime, reducing commissioning errors and evaluating multiple design options before installation. Instead of building and modifying physical systems one step at a time, manufacturers can evaluate multiple scenarios virtually and choose the best path forward.
The result is fewer surprises when systems go live and greater confidence in capital investments. In many cases, Wise said, manufacturers are seeing payback in one to three years.
The digital backbone: data makes or breaks the twin
Both Wise and Switzer emphasized that digital twins live or die based on data availability and integrity. Without a strong digital backbone — often referred to as a unified namespace (UNS) — a digital twin cannot scale or deliver long-term value.
“If you want enterprise-wide visibility, you need data from the lowest-level device all the way up to IT systems,” Switzer said. “Historically, that data lived in silos. Today, new UNS products make it much easier to connect everything into one coherent structure.”
Wise added that this foundation is also critical for AI readiness.
“If you’re going to use AI, you have to have clean, connected data,” he said. “Building a digital twin is one of the first real steps toward being ready for AI-driven manufacturing.”
Many facilities still lack full connectivity, with only 60-80% of devices tied into centralized systems. Closing that gap is often the first step E Tech Group takes with clients beginning their digital twin journey.
Scaling digital twins across multiple sites
While building a digital twin for a single line or facility can deliver immediate value, scaling across multiple sites introduces new challenges. According to Wise, success requires both executive alignment and the right integration partner.
“You need C-suite buy-in that this is a company-wide initiative,” Wise said. “And you need a partner who can operate across geographies and technology stacks.”
Switzer highlighted the technical complexity of standardization, noting that different regions often rely on different control platforms and design practices.
“One site might use Siemens, another Rockwell, and each integrator structures data differently,” he said. “The challenge is creating a unified model that works across all of it. New tools and standards are making that possible, but it still requires discipline.”
From de-risking projects to remote operations
Today, most manufacturers adopt digital twins to reduce risk during upgrades or expansions. However, Wise sees the technology evolving into a permanent operational asset.
“The real value comes when the digital twin is always there,” Wise said. “Not just for one project, but as a living model of the facility.”
That opens the door to remote monitoring, faster troubleshooting and predictive improvements. With live data feeding into a digital twin, integrators and manufacturers can diagnose issues without travel, test fixes virtually and deploy solutions with confidence.
Are lights-out factories finally realistic?
The concept of a fully automated, lights-out factory has been discussed for decades. Wise believes the first true examples are now within reach — under the right conditions.
“Brand-new facilities designed from the ground up have a real chance of being lights out,” he said. “We’re already seeing that thinking in life sciences and data centers.”
Brownfield sites, particularly in food and beverage or CPG, will take longer to reach that level of autonomy. Still, digital twins, remote monitoring and connected data are essential steps along that path.
From concept to competitive advantage
Perhaps the most significant shift in digital twin adoption is cultural. As Switzer noted, buy-in often comes quickly once operators and engineers see what digital twins can do.
“When people on the floor see their process represented digitally, the value becomes obvious,” he said. “It changes how they think about what’s possible.”
As digital twins move from experimental projects to foundational tools, manufacturers are discovering they are no longer just a visualization technology but a competitive advantage.
The Ctrl+Alt+Mfg Podcast
Make sure to check out the first few episodes of the Ctrl+Alt+Mfg podcast, where hosts Gary Cohen and Stephanie Neil discuss a range of digital transformation insights:
Ep. 1: Resetting and Rethinking Manufacturing
Ep. 2: Uniting Disparate Data With John Lee, Matrix Technologies
Ep. 3: Rethinking OT Security With Leah and Jeremy Dodson, Piqued Solutions
Ep. 4: Making Digital Transformation Real With Alicia Lomas, Lomas Manufacturing
Ep. 5: Reducing MES Project Risk With Ryan Crownover, Vertech
Ep. 6: Digital Transformation – Hype, Reality & What’s Next With Mike Ouellette, Engineering.com
