Digital Twins for Industrial Process Optimization and Asset Reliability

The basic challenges in manufacturing haven't changed much over decades: maintaining quality, keeping equipment running, maximizing throughput, and staying flexible. What has changed is how urgent solving these problems has become. Supply chain volatility and operational constraints make modern solutions necessary rather than optional.

Erik Udstuen, CEO of TwinThread, started his career as a process engineer in the pulp and paper industry before founding multiple companies focused on historians, manufacturing execution systems, and industrial analytics. His perspective on digital twins comes from years of building systems that actually work in production environments.

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Three Layers Where Digital Twins Create Value

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Digital twins cut across three distinct areas of manufacturing operations. Understanding these layers helps clarify where digital twins fit in your overall data strategy.

Industrial DataOps: This involves collecting OT data, adding context, enriching it, and preparing it for analysis. Your digital twin provides the structure for this work. Without a digital representation of your equipment and processes, you're just moving raw data around.

Industrial AI Operations: Once you have curated data, you can apply AI to drive optimization. This is where digital twins become particularly valuable. The twin provides the industrial context that generic AI algorithms lack. Time series data and its volume make industrial AI different from marketing or sales applications. Your algorithms need to understand what a pump or reactor or production line actually does.

Enterprise Integration: Plant floor data needs to flow into enterprise workflows like production planning, R&D, and quality development. Digital twins provide the common language between operational systems and business systems. When planning needs to understand capacity constraints or R&D wants to test process changes, the digital twin represents what's actually happening on the floor.

The digital twin concept ties all three layers together. You need a digital representation of your physical equipment and processes that works consistently across data collection, AI analysis, and enterprise integration.

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Data Sources for Complete Digital Twins

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Building a complete digital twin requires data from three main sources, each with different characteristics.

Legacy automation systems still provide most sensor data. OPC protocols remain the most common interface. Your PLCs and DCS systems have been collecting this data for years.

Smart sensors add capabilities outside traditional automation. These devices connect through MQTT or other message protocols. They measure things that weren't practical to wire into your control systems - vibration on motors, temperature at difficult access points, environmental conditions. This category keeps growing as sensor costs drop and connectivity improves.

Higher level systems like manufacturing execution systems and quality systems provide context that sensors can't capture. When did a batch start? What was the recipe? What quality tests were performed? This data doesn't come from sensors but it's essential for understanding what your process is actually doing.

The completeness of your digital twin depends on integrating all three sources. Process data alone doesn't tell you why quality changed. Quality system data alone doesn't show you what process conditions caused the problem. You need both.

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Process Twins and Asset Twins: Use Case Driven Approaches

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The distinction between process twins and asset twins matters because it drives your data collection strategy.

For a quality optimization use case, you need process data showing how your operation runs, quality system data showing what you produced, and potentially smart sensor data from devices like near-infrared analyzers that sit outside traditional systems. The use case determines which data sources matter.

For an asset reliability use case, you need motor current draw, vibration measurements, temperature readings, and operating context. Vibration sensors usually come through smart devices rather than traditional automation. Again, the use case drives what you collect and how.

This use case driven approach has practical implications. Don't try to model everything before you start. Pick a specific problem - maybe a quality issue on one production line - and build the digital twin that solves that problem. Then expand based on what you learn.

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Working with Historians as Data Sources

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When training AI models, you need months of historical data. Three, six, or twelve months depending on the use case. This makes historians particularly important for digital twin implementations.

Each historian system has its own API for data access. PI System, Wonderware, GE, Aspen Tech IP21 - they all work differently. This creates integration challenges but it also means you can start adding value on top of data you're already collecting.

The best situation is when historical data already exists. You can begin training models and proving value without waiting months for data collection. This accelerates time to value significantly.

Each historian also models data differently. Understanding these differences matters when you're building digital twins that need to work consistently across multiple sites or multiple systems.

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Data Modeling Strategies for Digital Twins

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Two main approaches exist for organizing digital twin data: asset hierarchies and semantic modeling.

Asset hierarchies organize equipment and processes in parent-child relationships. This matches how most people think about their facility - plant contains lines, lines contain machines, machines contain components. It's intuitive and easy to navigate. Most historians and MES systems already organize data this way.

Semantic modeling adds relationships beyond simple hierarchies. It captures how things relate to each other - this motor drives that pump, this sensor measures that process variable, these conditions affect that quality parameter. Semantic models create graphs rather than trees.

Both approaches have value. Asset hierarchies work well for navigation and basic organization. Semantic models become important for AI applications that need to understand relationships. Many implementations use both - hierarchies for structure, semantic models for analysis.

The key is starting simple and adding complexity only when use cases require it. Don't build elaborate semantic models before you know what problems you're solving.

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Balancing First Principles and Machine Learning

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Digital twins can incorporate both physics-based models and machine learning models. Knowing when to use each approach saves time and delivers better results.

First principles models work well when you understand the underlying physics and the relationships are relatively stable. Energy balances, material balances, thermodynamic relationships - these don't change. A model built on first principles will keep working as long as your equipment operates within its design range.

Machine learning models work well when relationships are complex or poorly understood. Quality issues that depend on subtle interactions between variables, degradation patterns in equipment, optimization opportunities that involve many competing factors - machine learning can find patterns that aren't obvious from first principles.

Many effective digital twins combine both approaches. Use first principles where physics provides clear relationships. Use machine learning where empirical patterns matter more than theoretical understanding. The combination often works better than either approach alone.

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Time to Value Through Prebuilt Applications

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Building a comprehensive digital twin platform requires significant investment. Organizations need to prove value before committing to enterprise deployments. This creates a challenge: how do you demonstrate what's possible without building everything first?

Prebuilt applications solve this problem. Instead of starting with a development platform and building applications from scratch, you can deploy applications configured for specific use cases. Quality optimization, batch process optimization, asset reliability - each has common patterns that can be packaged into applications.

These applications prove value quickly. You can demonstrate improved quality or reduced downtime in weeks rather than months. Once you prove value, you can justify the larger investment in enterprise deployment.

This approach also helps identify what capabilities your digital twin platform actually needs. You learn what data sources matter, what models work, and what integration points are essential. This learning guides better decisions about enterprise architecture.

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Integration with Enterprise Systems

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Digital twins become more valuable when they connect to enterprise workflows. Production planning needs to understand actual capacity and constraints. R&D needs to test process changes. Quality development needs to understand what drives variation.

Each integration serves different purposes. Planning integration provides realistic production schedules based on actual equipment capabilities. R&D integration enables virtual testing before physical trials. Quality integration closes the loop between what you measure and what you make.

The common thread is using the same digital representation across all these activities. When planning and operations work from the same model of what equipment can do, schedules become more realistic. When R&D and operations use the same process models, laboratory improvements translate to production more reliably.

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Practical Considerations for Digital Twins Implementation

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Several factors affect digital twin implementation success.

Start with existing data collection. If you already have historians collecting data, use that as your foundation. Don't wait to deploy new sensors before you start. Build on what exists and add data sources as use cases require them.

Let use cases drive data requirements. Don't try to collect everything. Pick specific problems and collect the data needed to solve them. Expand as you prove value and identify new opportunities.

Plan for different skill sets. Your team needs process engineers who understand operations, data scientists who can build models, and software engineers who can handle integration. The platform needs to support all three groups without requiring everyone to become programmers.

Prove value before scaling. Implement one use case successfully before rolling out across multiple sites. Learn what works and what doesn't. Use those lessons to guide enterprise deployment.

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Building Cross-Functional Teams

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Digital twin implementations require teams with diverse expertise. Process engineers understand how equipment and processes actually work. Data scientists know how to build and train models. Software engineers handle integration and deployment.

These different perspectives all matter. Process engineers identify what's physically possible and what constraints exist. Data scientists find patterns in data and build predictive models. Software engineers make everything work together reliably.

The challenge is enabling these different roles to work together effectively. Your digital twin platform needs to support process engineers building first principles models, data scientists training machine learning models, and software engineers deploying applications - ideally without requiring everyone to master the same programming languages and tools.

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Looking Forward

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Digital twins in manufacturing are moving beyond proof of concept. Organizations are deploying them for actual optimization and reliability improvements. The technology works. The question now is how to deploy it effectively at scale.

Success comes from starting with clear use cases, building on existing data infrastructure, proving value quickly, and then scaling based on what you learn. This isn't about implementing the most sophisticated possible solution. It's about solving real problems in ways that deliver measurable business value.

The manufacturers succeeding with digital twins treat them as tools for continuous improvement rather than projects with defined end states. They start small, prove value, expand systematically, and keep refining based on results. That approach works regardless of which specific technologies or platforms you choose.

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