While the idea of a greenfield smart factory is appealing, it’s simply not the reality for most companies. What they need isn’t a futuristic leap—but a practical step forward. That means modernizing existing operations to build a factory of today.
From Legacy to Leadership: Building the Manufacturing Company of Today
Jan Burian, Head of Industry Insights | Trask
In today’s industrial landscape, there are only two kinds of manufacturing companies: those that turn data into value and competitive advantage—and those that don’t. Period.
Yet many manufacturers remain stuck. Some are too focused on legacy processes and past decisions; others feel overwhelmed by the bold promises of the “Factory of the Future.” As a result, they launch isolated digital initiatives without building a cohesive platform to manage data. Without this foundation, it’s nearly impossible to scale business applications or unlock the full potential of AI.
While the idea of a greenfield smart factory is appealing, it’s simply not the reality for most companies. What they need isn’t a futuristic leap—but a practical step forward. That means modernizing existing operations to build a factory of today, capable of evolving toward tomorrow.
Why So Many Manufacturers Struggle to Become Truly Smart
Several well-known barriers continue to slow down digital progress across the industry:
1. Legacy IT Environments and Technical Debt
Outdated systems often lack the agility and integration capabilities that modern business demands.
2. Limited Digital Skills and Mindset
Digital transformation isn’t just an IT challenge—it requires people across all functions to think and act digitally.
3. Speed of Change vs. Risk Appetite
Fear of failure can lead to decision paralysis. Manufacturers need the ability to “test fast and scale safely.”
Beyond these core issues, many organizations also wrestle with unclear return on investment, ongoing external disruptions, and ineffective governance around digital and optimization projects.
What Manufacturing Leaders Want
In my conversations with manufacturing CEOs, CFOs, COOs, and CIOs, their goals may differ slightly, but the direction is clear: satisfy both internal and external customers while delivering sustainable profit.
To do this, companies must:
- Drive innovation
- Optimize resources
- Improve quality and throughput
- Stay resilient in the face of frequent disruptions
Most factories don’t have the luxury of halting operations for weeks to upgrade infrastructure, software, or hardware. And even organizations considered digitally mature are often siloed, with fragmented systems leading to inefficient planning and rigid operations. In complex production environments, last-minute changes to components or configurations remain difficult.
Designing the Manufacturing Company of Today
Key elements organizations should consider when designing digitally and data-driven operations today include:
1. Industrial Data Fabric
A foundational framework that enables ingestion, contextualization, and actionable insights from both industrial and enterprise data. Bridging the gap between operational (OT) and enterprise (IT) data allows for AI-driven decisions and a unified business view.
2. Converged IT/OT Architecture
The traditional automation pyramid is evolving into a cloud/edge/intelligent field structure. This supports AI-enabled automation and autonomous operations.
3. Modernized Enterprise Systems
- MES: Moving from plant-centric to enterprise-wide orchestration. Modern MES focuses on modularity, user experience, and plug-and-play capabilities using low-code/no-code tools.
- PLM: Gartner predicts that by 2026, more than 80% of PLM platforms will include embedded AI. To stay ahead, organizations should prioritize AI enhancements in areas such as product planning, concept development, and model-based design.
Dr. Adrian Reisch, Partner at EY Consulting and PLM expert, explains:
“The future of PLM lies in building a connected, intelligent ecosystem where design, production, and service data interact seamlessly. Manufacturers that view PLM as a strategic data hub—rather than a static tool—will unlock faster innovation, higher product quality, and more sustainable value creation.”
- ERP: Modern ERP systems have evolved into cloud-based data and integration platforms that enable process automation, AI-driven insights, and seamless orchestration across operations. Key capabilities now include process mining, low-code/no-code development, generative AI copilots, and scenario-based planning models.
Maggie Slowik, Global Industry Director – Manufacturing at IFS, shares her perspective on AI-powered ERP: “AI has ignited a race in industrial markets—and in manufacturing, modern ERP systems with embedded AI are the engine driving that transformation. These systems don’t just analyze data—they act, using agentic AI to enable real-time decisions that reduce waste, optimize production and supply chains, and boost agility.”
4. Industrial AI-Enabled Use Cases
Unlocking value from use cases like predictive maintenance, AI-enhanced quality control, and real-time automation depends on access to integrated, up-to-date OT and IT data. More than 50% of AI projects in manufacturing can even fail due to a lack of contextualized data—making this capability essential for achieving tangible benefits from industrial AI.
5. Connected and Mobile Workers
Empowering frontline staff with real-time data, copilots, and natural language interfaces creates smarter, safer, and more productive operations.
Wrap-Up & Recommendations: Turning Vision Into Execution
In today’s manufacturing landscape, success doesn’t belong to the strongest or the most capitalized—but to those who can adapt fastest by turning data into value. The challenge is not just about transformation from the ground up, but modernization with intent. Most manufacturers don’t have the luxury of a greenfield factory or months of downtime. They need practical, phased strategies that modernize operations while keeping the business running.
Here’s how to get started:
1. Modernize Before You Transform
Stop chasing the ideal future state. Instead, stabilize and modernize your current landscape. Build a resilient "factory of today" as the foundation for tomorrow's innovations.
2. Bridge IT and OT with an Industrial Data Fabric
Break data silos and create real-time, contextualized visibility by integrating IT and OT systems. This enables intelligent planning, predictive maintenance, and scalable AI use cases.
3. Platformize ERP, MES, and PLM
Rethink enterprise systems as platforms, not isolated tools. Prioritize integration, user experience, and AI capabilities. Choose modular, cloud-native solutions with pre-built industry content to accelerate time-to-value.
4. Adopt Software-Defined Automation
Transition from rigid control hierarchies to flexible architectures that combine cloud, edge, and field-level intelligence. This is the bedrock for AI-driven automation and future autonomy.
5. Focus on Data Quality and Context
Invest in harmonized data models and digital threads that reflect the reality of your operations.
Predrag (PJ) Jakovljevic, Principal Industry Analyst at Technology Evaluation Centers, offers his viewpoint on value chain visibility: “A complete digital thread—not just within the company, but spanning the entire supply chain—is critical. The ability to simulate changes across the supply chain, make corrections, and only then commit is truly mind-blowing.”
6. Empower People with Technology
Think like Ironman, not Superman. It’s not about doing everything alone—it’s about being empowered by systems that guide, assist, and accelerate decision-making. Connected workers, copilots, and mobile-first tools should become part of your digital execution strategy.
7. Ensure Cross-Functional Accountability
Organizations that succeed digitally do so because their CIOs, COOs, CFOs, and other leaders share end-to-end responsibility. Digital execution must be jointly owned to drive consistent value.
8. Wrap It All Up into a Comprehensive Digital Twin
Capture both product and process data in a digital twin that offers real-time visibility into product status, production planning, and customer-facing insights.
According to Daniel Pecina, Chief International Business Officer at Trask, who shares his experience in delivering digital twin solutions within the automotive industry:
“Digital twin capabilities—with a direct impact on inventories and customer satisfaction—should enable end-to-end transparency across the full lifecycle of a product order, from product configuration and order entry to production and delivery.”
Remember: Be Ironman, Not Superman
Ironman wasn’t a superhero by nature—he was a technologist empowered by J.A.R.V.I.S., his AI copilot. That’s the model for today’s manufacturers: humans empowered by intelligent tools!
The content & opinions in this article are the author’s and do not necessarily represent the views of ManufacturingTomorrow
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