Editor's Note: This article was co-authored with Thomas Kuckhoff, an expert from Omron, who brings industry-leading insight into how digital twins are transforming modern manufacturing.

What if you could spot equipment issues early, improve processes without stopping production, and speed up product development? It sounds like a pipe dream, but that’s exactly what digital twins can help manufacturers do.

These digital replications mirror physical systems in real-time, enabling manufacturers to optimize equipment performance, test solutions offline to reduce waste and implement improvements across operations without disrupting production.

In this guide, we’ll define digital twins, explore their fundamental principles, and highlight how manufacturers can leverage digital twin technology to streamline operations and reduce costs.

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Understanding Digital Twin Technology | The Four Types of Digital Twins in Manufacturing | Choosing the Right Digital Twin for Your Manufacturing Goals | How Digital Twins Improve Manufacturing Processes | Applications of Digital Twins in Manufacturing | Deploying Digital Twins in Your Facility | Additional Resources 

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Understanding Digital Twin Technology in Manufacturing 

A digital twin is an exact virtual copy of an object or system created using Internet of Things (IoT) data, enabling manufacturers to represent, diagnose, predict, and optimize operations in near real-time.

Imagine having a digital environment where every aspect of your manufacturing line is mirrored, allowing you to observe interactions, diagnose issues, and even predict future performance using digital twin data. This is the power of digital twin technology in manufacturing.

The concept of the digital twin has evolved significantly since its inception. Originally coined in 1998 to refer to a digital replica of a famous actor's voice, the term gained traction in manufacturing due to its immense potential to enhance operational efficiency. Digital twins help manufacturers use team insights, test changes virtually and make informed decisions—without trial and error on the shop floor.

Historical Evolution and Trends

The roots of digital twin technology can be traced back to NASA’s practices in the 1960s. Still, it wasn’t until a 2002 presentation at the University of Michigan that the concept began to gain prominence in the manufacturing industry. Since then, the market for digital twins has seen exponential growth, with projections indicating a market size of over $73.5 billion by 2027, growing at approximately 30% annually from 2023 to 2027.

This rapid adoption has been driven by several key advancements—faster and more affordable computing power, the rise of CAD software, and smarter algorithms that can closely mimic real-world physics. Together, these building blocks have made it possible to create accurate, data-rich digital environments that are easier to use and more powerful than ever before.

Key Technologies Enhancing Digital Twins in Manufacturing

Key technologies such as artificial intelligence (AI) and big data analytics play a crucial role in enhancing the functionality and integration of digital twins in manufacturing processes. Additionally, the emergence of the IoT has made digital twins more accessible and essential for modern businesses. IoT connects devices across the manufacturing line, collecting large amounts of data that feed into the digital twin, ensuring it accurately mirrors the physical environment.

Together, these technologies create a digital ecosystem where digital twins offer real-time insights, predict outcomes, and support better decisions.

As this ecosystem evolves, manufacturers are also beginning to leverage synthetic data—computer-generated data sets used to simulate real-world conditions. This opens the door to identifying rare scenarios or “grey swans,” helping teams anticipate unexpected challenges and uncover insights that may not be visible in historical data alone. 

The Four Types of Digital Twins in Manufacturing

As manufacturers integrate digital twin technology, it’s important to understand that not all digital twins operate at the same level—or with the same scope. In industrial manufacturing, digital twins can vary by scale (such as the number of connected devices or production lines), resolution (the level of detail or granularity of data, like defect detection or part-level monitoring), and deployment (whether they run on the edge or in the cloud).

Digital twins are often categorized into four distinct types, each designed to optimize operations at a different layer of the manufacturing process. When used together, they offer a more complete, real-time view of the production environment—enabling better decision-making, predictive insights, and continuous improvement.

If you're considering implementing digital twins in your facility, it’s important to select the right type(s) based on your goals—whether you're aiming to reduce downtime, increase throughput, or improve product quality.

Here’s a breakdown of the four key types and how they fit into this broader framework:

1. 1. Component or Part Twins – Monitoring Individual Equipment & Parts
   
   

Component twins represent the smallest level of digital twin technology, focusing on individual machines, components, or assets. These digital replicas allow manufacturers to:

✔️ Monitor equipment performance in real-time, enabling teams to track stress over time and estimate fatigue life—critical for preventing breakdowns and optimizing maintenance schedules. 

✔️ Run stress tests and simulate wear and tear to anticipate equipment failures—shifting maintenance strategies from reactive fixes to proactive, predictive planning.

✔️ Predict potential failures and optimize maintenance schedules by understanding how long equipment can safely operate under current conditions—enabling smarter decisions around production changeovers and trend-based servicing.

For example, a component twin of a conveyor belt motor can use real-time data—such as temperature, vibration, and energy consumption—to simulate performance and predict potential issues before they cause downtime. This allows engineers to accelerate timescales, test interventions virtually, and optimize for key KPIs like efficiency and uptime.

2. Asset or Product Twins – Managing Equipment Groups & Subsystems

Asset twins build upon component twins by integrating multiple parts into a larger, functional unit—such as a robot arm, a CNC machine, or an entire packaging line. In manufacturing, an asset typically refers to equipment or machinery that holds book value and contributes to production. Asset twins simulate how components interact within the asset, helping teams ensure:

✔️ Reduced mean time between failures (MTBF)

✔️ Improved energy and resource efficiency

✔️ Optimized equipment performance across subsystems

For example, in a food processing plant, an asset twin could simulate how heating elements, conveyor belts, and ventilation systems interact to maintain consistent product quality while minimizing energy use. Engineers can run “what if” scenarios—such as adjusting airflow or belt speed—to test outcomes virtually before making changes on the floor.

3. System or Unit Twins – Enhancing Process Synchronization

System twins take a broader approach, connecting multiple asset twins to simulate and optimize entire production systems. Manufacturers use system twins to:

✔️ Enhance workflow coordination across multiple machines

✔️ Reduce bottlenecks in production by analyzing real-time data

✔️ Test new process changes before implementation

For example, a system twin in an automotive plant might connect the engine assembly line, robotic welding units, and quality inspection stations, allowing manufacturers to identify inefficiencies and optimize material flow. At this scale, resolution becomes critical—too little detail limits insight, but overly complex models can slow performance and provide diminishing returns. The key is balancing granularity with actionable value.

4. Process Twins – Optimizing Entire Manufacturing Facilities

Process twins operate at the highest level, simulating entire plant operations or even multiple factory locations. These digital models help plant managers:

✔️ Improve end-to-end supply chain visibility—for example, by tracking on-time delivery of raw materials through to finished goods output, helping identify bottlenecks and optimize planning.

✔️ Adjust production schedules to match demand fluctuations and make the most of available resources. For example, if an operator doesn’t show up for a shift, a digital twin can help determine whether to proceed with the original plan or adapt in real-time to maintain efficiency.

✔️ Identify and reduce inefficiencies in workflow timing and coordination—pinpointing delays, misalignments, or resource conflicts that slow production.

For example, a process twin in a chemical plant could simulate how raw material inputs, temperature settings, and reaction times impact final product yields. This insight enables manufacturers to fine-tune processes while minimizing waste and energy consumption.

However, because real-time simulations can be data-heavy, choosing where the digital twin is hosted—on the cloud, at the edge, or in a fog computing environment—impacts both performance and responsiveness. The right setup depends on how quickly decisions need to be made and how sensitive the data is.

Why It Matters: Choosing the Right Digital Twin for Your Manufacturing Goals

Selecting the right type of digital twin depends on your specific manufacturing objectives: 

By understanding and applying the right digital twin strategy, manufacturers can gain insights, improve efficiency, and stay competitive.

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How Digital Twins Improve Manufacturing Processes

Predictive insights from digital twins help manufacturers make informed decisions, streamline processes, and boost overall production efficiency. Imagine reducing development times by approximately 20-50% while simultaneously improving product quality and performance—this is the transformative potential of digital twins.

The core benefits of digital twins in manufacturing can be broadly categorized into three areas:

✔️ Accelerated product development

✔️ Enhanced process optimization

✔️ Improved production efficiency

Each of these benefits plays a critical role in driving operational excellence and will be explored in detail in the following subsections.

Accelerated Product Development

Digital twins significantly expedite various aspects of the manufacturing process, including design, development, testing, and maintenance. By providing real-time insights, digital twins in manufacturing support quick and informed production decisions, reducing the time to market and allowing manufacturers to respond swiftly to market demands. This acceleration in product development is crucial in today’s fast-paced market.

Additionally, digital twins help manufacturers save time and costs by minimizing the number of prototype iterations required. Creating accurate digital models with advanced software and existing blueprints allows manufacturers to simulate and test designs virtually, resulting in more efficient and cost-effective product development.

Enhanced Process Optimization

Digital twins help manufacturers optimize materials and processes to maintain consistency and reduce variation. Data models help predict outcomes and guide timely adjustments for better efficiency. With AI, this ability to predict and optimize is further enhanced, which means digital twins can spot more opportunities for improvement. 

On top of that, digital twins help replicate optimal production conditions, ensuring better performance consistency across manufacturing batches. With the integration of IoT sensors, digital twins offer real-time monitoring, ensuring quality standards are met throughout the manufacturing process.

🔎 Explore our selection of IoT sensors for digital twins in manufacturing.

Improved Production Efficiency

Continuous monitoring enabled by digital twins aids in identifying areas for improvement in manufacturing efficiency. Rather than serving as a permanent source of truth, a digital twin offers a snapshot of production data at a given point in time—establishing a benchmark that teams can use to measure progress, test changes, and drive continuous improvement.

Real-time insights generated by digital twins in manufacturing help streamline workflows and maximize productivity, driving operational efficiency. This powerful tool allows manufacturers to optimize their production processes, reduce energy consumption, and achieve higher product quality and performance.

Applications of Digital Twins in Manufacturing

The practical applications of digital twin technology in manufacturing are vast and varied. By enabling manufacturers to simulate processes and assess performance before physical production, digital twins in manufacturing enhance operational efficiency and innovation.

From predictive maintenance to virtual commissioning and quality management, digital twin applications offer numerous applications that drive manufacturing excellence.

1. Design: Virtual commissioning enables manufacturers to test and verify system integration in a simulated environment before physical implementation—ensuring smoother transitions and reducing operational disruptions.

2. Validate: Predictive maintenance allows for continuous monitoring and early identification of equipment issues, helping validate performance and reduce unplanned downtime and maintenance costs.

3. Improve: Quality management benefits from real-time monitoring and defect detection, leading to ongoing improvements in product quality and system performance.

4. Scale: Once systems are optimized, insights from digital twins help scale improvements across lines or facilities with greater confidence and consistency.

Next, let's dive deeper into three common digital twin applications manufacturers use to bring these benefits to life.

Predictive Maintenance

As explored earlier under the Component Twins section, digital twins use real-time data to simulate and predict equipment failure. Here’s how that plays out in a dedicated predictive maintenance strategy. For example, a digital twin improves reliability and reduces unplanned downtime when focused on specific goals, like minimizing finished good defects or aligning with planned maintenance windows.

Optimizing load levels through the insights provided by digital twins in manufacturing contributes to lowering overall maintenance costs. This proactive approach to maintenance not only enhances equipment longevity but also improves overall operational efficiency.

Learn more about predictive maintenance strategies in our blog post, Top Benefits of Predictive Maintenance: Boost Efficiency & Cut Costs. Or learn more in our video, The Why & How of Predictive Maintenance.

Virtual Commissioning

Virtual commissioning is a process that allows manufacturers to test and verify system integration before physical implementation. Accurate digital models from existing blueprints or manually developed models allow manufacturers to predict and resolve issues during system integration, leading to smoother transitions to physical setups.

This ability to simulate and test systems in a digital environment ensures potential problems are identified and addressed early—reducing the risk of operational disruptions and improving overall system performance. It also helps avoid costly commissioning failures after a product is built, reduces travel expenses for remote collaboration, and can accelerate compliance with industry regulations.

Quality Management

Digital twins significantly enhance quality management by providing manufacturers with better predictive capabilities to anticipate and mitigate potential quality issues. Real-time quality control through digital twins enables manufacturers to monitor production processes and quickly identify defects—reducing rework and scrap. The more precisely failure modes are documented, the fewer deviations occur, leading to more consistent product quality and fewer disruptions downstream.

Improved testing and validation of products are made possible through the ability to evaluate new solutions in diverse real-world scenarios. This proactive approach improves materials and methods for better products and happier customers.

Deploying Digital Twins in Your Facility

Digital twin technology is reshaping the future of manufacturing—enabling predictive insights, streamlined processes, and smarter decision-making. But unlocking its full potential requires more than just data—it takes deep industry expertise and the right implementation strategy.

That’s where we come in! Our team of experts and partners helps you move beyond theory to real-world impact, building digital twin models that are practical, scalable, and tailored to your operations. If you're ready to explore how digital twins can accelerate your performance, let’s talk.