AI & Digital Twins For Enhancing Manufacturing Workflow Efficiency

Industrial AI doesn't need more data, it needs a digital twin. A proper model where all that rich information is actually linked together in ways that make sense.

Andrew Scheuerman, CEO of Arch Systems, saw this firsthand building semiconductor equipment before founding a data and AI company for manufacturing. His insight: the barrier isn't AI capability. It's that factories generate massive amounts of data from "lots of little pipes," but that data sits disconnected. Give AI just the green/red status light from a machine, and it can't tell you much. Give it pressure values, error codes, maintenance history, test parameters, and operator observations, all properly linked, and suddenly it becomes transformative.

An automotive manufacturer proved this when Arch Systems helped them avoid $7 million in unnecessary capex spending by using AI to find and fix bottlenecks that were limiting throughput by 30%. The AI didn't do anything magical, it just had access to properly linked data that humans couldn't process at scale.

‍

‍

The Real Barrier to AI Success in Manufacturing

‍

Before diving into solutions, it's important to understand why so many AI initiatives fail to deliver. The answer comes down to three fundamental challenges:

The Data Problem: Most factories generate massive amounts of data, but it exists in isolated silos. Your machine data lives in one system, quality measurements in another, and operator notes in yet another location. When an AI needs to understand why a line went down, it can't connect these dots because the information isn't linked together.

The Cultural Challenge: Many operators and line supervisors view new technology as a threat rather than a tool. They've spent years building expertise, and suddenly there's talk of AI replacing their knowledge. This resistance isn't irrational; it's a natural response to poorly communicated change.

The Investment Hesitation: Leadership sees headlines about expensive AI projects failing to deliver ROI. They hear about companies spending millions with little to show for it. The result? Decision paralysis, with companies either doing nothing or making small, ineffective bets that confirm their worst fears.

The solution to all three challenges starts with getting your data foundation right. And that's exactly what digital twins are designed to do.

‍

What Digital Twins Actually Do for Your Data Strategy

‍

Think of a digital twin as an organized filing system for your factory. Instead of having information scattered across dozens of disconnected systems, a digital twin creates a unified model that shows how everything relates to each other.

Here's what makes this approach powerful: when you map all your data into a digital twin structure, you're not just organizing information. You're creating context. The digital twin knows that Machine A feeds into Process B, which produces Part C that gets measured by Test D. When something goes wrong, the AI can now trace through these relationships to find root causes.

This isn't theoretical. One manufacturer using this approach discovered they were running unnecessary validation stops after every product changeover, a practice that dated back to an old customer requirement that no longer applied. Once they had visibility through their digital twin, they could see the real-time quality data and eliminate these stops. The result? A 90% increase in overall equipment effectiveness on affected lines.

Another example involved a company planning to spend $21 million on new equipment to meet customer demand. By using AI to analyze their digital twin data, they identified and resolved bottlenecks in their existing lines. They fulfilled their customer orders while cutting the equipment investment to $14 million, a $7 million savings that went straight to the bottom line.

‍

From Reactive to Proactive: The Four Levels of Analytics

‍

One helpful way to think about AI applications in manufacturing is through the lens of four types of analytics:

Descriptive Analytics: What happened? Digital twins make it far easier to collect and visualize data efficiently. Instead of manually pulling reports from multiple systems, you have a single source that describes your factory's performance in real time.

Diagnostic Analytics: Why did it happen? This is where AI starts to shine. Modern AI can automatically sift through massive amounts of data to identify root causes. For example, systems can now analyze operator notes, machine logs, and sensor data simultaneously to automatically determine whether downtime was caused by material shortages, equipment failures, or process issues.

Predictive Analytics: What will happen? Both traditional machine learning and newer generative AI approaches excel at forecasting. They can predict equipment failures, quality issues, and throughput problems before they occur, giving your team time to take preventive action.

Prescriptive Analytics: What should we do about it? This is where generative AI becomes uniquely powerful. Instead of just flagging a problem, these systems can create detailed guidance and playbooks for operators on how to solve specific issues. They combine your historical data with best practices to provide step-by-step instructions for resolving problems.

The key insight is that each level builds on the previous one. You can't get good prescriptive guidance if you haven't solved the diagnostic problem first. And you can't diagnose issues effectively if your descriptive data is scattered and disconnected.

‍

Practical Implementation: Where to Start and Who Should Lead

‍

The good news is that the barriers to adoption have never been lower. You don't need a complete digital transformation to begin seeing value. Here's a practical approach:

Start with the Right People: Look for individuals in your organization who sit at the intersection of IT and operations. These are often managers or directors who understand both technology and manufacturing processes. They might not be your most senior leaders, and that's okay. What matters is that they have the practical knowledge and the drive to make things happen.

Give Them Clear KPIs: Don't ask them to "explore AI" or "pilot digital twins." Instead, tie their work to specific business outcomes. Reduce unplanned downtime by 15%. Increase OEE by 10%. Cut quality escapes in half. These concrete targets focus the effort and make it easier to measure success.

Begin with Quick Wins: Identify one production line or process where you have good data and clear pain points. Use this as your proving ground. Success here builds momentum and creates internal advocates who can help scale the approach across your organization.

Leverage External Expertise Strategically: You don't need to build everything in-house. Partner with specialists who have already solved these problems. But make sure you're building internal capability at the same time. The goal is to learn and own the process, not just rent a solution.

One manufacturer started by implementing automatic downtime root cause analysis. Previously, operators had to manually classify every stoppage, which took time away from actually fixing problems. The new AI system analyzes operator notes and machine data automatically, categorizing stoppages by the traditional 4M+E framework (machine, method, man, materials, environment) without human input. This freed up operator time while providing more accurate and complete downtime data.

‍

The Workforce Transformation: Why Your Team Will Actually Want This

‍

There's a common fear that AI will replace workers or make jobs less fulfilling. The reality in manufacturing is quite different.

Today's workforce doesn't stay at one company for 20 or 30 years. That means the traditional model where knowledge lives in people's heads and gets passed down through mentorship is breaking down. New employees often complain that working in factories is difficult because nobody gives them proper guidance. They feel lost, they struggle, and they leave.

AI changes this dynamic. When an experienced operator's knowledge is captured in an AI system, it becomes available to everyone. New hires get the guidance they need to be productive quickly. The systems provide the kind of interface and support that modern workers expect. Rather than feeling threatened, employees report that they actually want these tools because they make their jobs easier.

This creates a positive cycle. Retention improves because people feel supported. Productivity increases because less knowledge is lost when someone leaves. And your company builds a sustainable competitive advantage that isn't dependent on a few irreplaceable individuals.

‍

Looking Forward: What's Coming Next

‍

The trajectory of AI in manufacturing is clear. Within the next decade, possibly sooner, factories that don't use AI will struggle to compete on cost and efficiency. This isn't meant to scare anyone, it's simply the reality of where technology is heading.

But here's the encouraging part: you don't need to get there overnight. Start small, learn what works in your environment, and build from there. The companies that will thrive are those that begin this journey now, even if they start with modest steps.

The future factory won't have AI doing 100% of the work. Instead, you'll see AI systems handling 20-30% of factory operations, working alongside people to make them more effective. This collaboration between human expertise and machine intelligence is where the real opportunity lies.

‍

Key Takeaways for Data and Analytics Leaders

If you're responsible for data and analytics in manufacturing, here are the practical actions you should consider:

First, audit your current data landscape. Map out where information lives and how (or if) it connects. Identify the biggest gaps where disconnected data is preventing insights.

Second, evaluate your organizational readiness. Who in your organization has both the technical capability and manufacturing knowledge to lead AI initiatives? These people might not be at the C-level, and that's perfectly fine.

Third, define specific business outcomes you want to achieve. Don't pursue AI for its own sake. Link every initiative to measurable improvements in throughput, quality, downtime, or cost.

Fourth, consider digital twins as your data integration strategy. Rather than trying to connect systems directly, use a digital twin as the organizing framework that brings everything together.

Finally, remember that this is a journey of continuous improvement. You won't transform everything at once. Focus on delivering value in stages, building momentum and capability as you go.

‍

Conclusion: The Time to Act Is Now

‍

The question isn't whether AI and digital twins will transform manufacturing. They already are. The question is whether your organization will lead this transformation or scramble to catch up.

The good news is that you don't need unlimited budgets or armies of data scientists to get started. What you need is a clear-eyed assessment of your data challenges, the right people empowered to solve them, and a willingness to learn and adapt as you go.

The manufacturers who recognize this opportunity and act on it will build sustainable competitive advantages. They'll attract and retain better talent. They'll operate more efficiently. And they'll be positioned to thrive in an increasingly competitive global market.

The foundations you build today in data infrastructure, AI capabilities, and organizational culture will determine your company's trajectory for the next decade. The question is: are you ready to start building?

‍

‍

‍