The Relationship Between the Physical World & Digital World
The concept of digital twins has evolved from a niche technology to a critical driver of Industry 4.0, enabling businesses to monitor, optimize, and automate their operations in real time. It’s no longer just a futuristic idea—digital twins are here, and they’re rapidly transforming industries.
This isn’t just hype—businesses are investing heavily in digital twins. According to IoT Analytics in their Digital Twin Market Report 2023-2027, the global digital twin market is expected to skyrocket from $418 million in 2022 to $1.5 billion by 2027, growing at a 29.4% annual rate. Companies in manufacturing, logistics, healthcare, and smart cities are all adopting digital twin technology to make smarter, faster, and more predictive decisions.
But despite the buzz, there’s still a lot of confusion about what actually qualifies as a true digital twin. Some people think it has to include a detailed 3D rendering of an asset or facility. Others assume it’s a specialized software solution that must be purchased separately. The reality? A digital twin is about function, not form.
A digital twin is simply a virtual representation of a physical system that continuously updates based on real-world data. It doesn’t have to be a 3D model, and it doesn’t require a dedicated software suite. In fact, many businesses already have the building blocks for a digital twin within their existing Manufacturing Execution System (MES), IoT infrastructure, or AI-driven analytics platforms—they just need to unlock its full potential.
The Three Essential Parts of a Digital Twin
A true digital twin is more than just a digital model—it’s a living, data-driven system that mirrors and influences the physical world. To function effectively, a digital twin needs three core components:
The Digital Definition (Virtual Model)
This is the blueprint of the asset, process, or system being mirrored. It’s usually created using CAD (Computer-Aided Design), PLM (Product Lifecycle Management), or process mapping tools. This virtual representation contains all the relevant specifications, but on its own, it’s nothing more than a static file.
Example: A 3D model of an engine in a design program—useful for visualization, but not a digital twin by itself.
Real-Time and Historical Data Integration
A real digital twin isn’t static—it ingests live data from IoT sensors, telemetry systems, and historical records. This allows it to reflect real-world conditions and predict future outcomes.
Example: A factory machine with IoT sensors that sends live performance data (temperature, vibration, output levels) to its digital twin, allowing for real-time monitoring.
Analytics, AI, and Simulation Layer
The real power of a digital twin lies in its ability to analyze, simulate, and optimize operations. This layer processes incoming data, runs predictive models, and even suggests (or autonomously makes) adjustments to improve performance.
Example: An AI-driven digital twin of a wind turbine that simulates upcoming weather patterns and automatically adjusts settings to maximize efficiency.
Three Levels of Digital Twin Integration
Not all digital twins operate at the same level of sophistication. Depending on how deeply integrated they are with their physical counterpart, they fall into one of three categories:
Digital Model – The Static Blueprint
At the most basic level, a digital model is a digital representation of a physical object or process, but it does not receive live updates or interact with its real-world counterpart. It is useful for design, documentation, and process mapping, but it remains purely informational. Because it lacks real-time connectivity, it does not qualify as a true digital twin—it’s simply a digital version of something that exists physically.
Example: A 3D layout of a factory floor that helps engineers plan equipment placement but does not update as conditions change.
Digital Shadow – One-Way Connection
A digital shadow takes things a step further by incorporating one-way data flow from the physical object to its digital counterpart. This means that the digital model updates in response to real-world changes, but it cannot send commands back to influence the physical system. This type of digital twin is commonly used for monitoring, diagnostics, and predictive maintenance, where real-time tracking is valuable, but direct intervention is still manual.
Example: A predictive maintenance system for factory machines that collects sensor data and alerts operators when a component is close to failure, but requires a person to take action.
Digital Twin – A Fully Integrated, Two-Way System
A true digital twin goes beyond passive monitoring and creates a bi-directional feedback loop between the digital and physical worlds. Changes in the physical asset automatically update the digital twin, but the key difference is that the digital twin can also influence the physical system, making real-time optimizations based on AI-driven insights. This level of digital twin is what enables automated process control, adaptive manufacturing, and intelligent decision-making without requiring human intervention.
Example: Tesla’s digital twin technology, which allows the company to monitor every car in real-time, diagnose potential issues, and push software updates that optimize performance and efficiency remotely.
Why Your MES Should Be a Digital Twin (And If It’s Not, Something’s Missing)
A Manufacturing Execution System (MES) is the core of modern manufacturing operations management—it’s the system that connects, tracks, and optimizes everything happening on the shop floor. In many ways, an MES is already a key enabler of Industry 4.0, providing real-time visibility into production, ensuring quality control, and orchestrating workflows across machines, workers, and supply chains. But what many manufacturers don’t realize is this: A well-implemented MES should, by its very nature, create a digital twin of your manufacturing process.
MES as a Digital Twin—Not Just a Data Tracker
A true digital twin isn’t a separate, standalone piece of software—it’s a capability, not a product. And if your MES is doing its job right, it should already be acting as a digital twin of your factory floor.
Why? Because a digital twin is simply a virtual representation of a real-world system that stays updated in real-time, allowing for continuous monitoring, analysis, and optimization. That’s exactly what a modern MES is built to do.
Think about it:
Your MES tracks every step of production—from raw materials to finished products.
It collects real-time data from IoT sensors, machines, and operator inputs.
It analyzes performance, detects inefficiencies, and triggers automated responses.
It integrates with ERP, PLM, and supply chain systems to create a complete picture of manufacturing operations.
That’s a digital twin. It may not be called one, but if your MES is designed well, you’re already living in a digital twin environment. One of the biggest misunderstandings about digital twins is the idea that they must be a fully rendered 3D model of a machine or process. That’s not true.
While some digital twins do incorporate 3D visualizations for specific use cases (e.g., virtual reality training, facility simulations, or complex spatial analysis), this is not a requirement. A digital twin is about data and connectivity, not graphics. A good MES creates a functional, data-driven digital twin by:
Synchronizing live machine and process data (temperatures, speeds, outputs, faults, etc.).
Mapping process flows dynamically as conditions change.
Triggering automated responses based on predefined logic or AI-driven insights.
Allowing bidirectional data exchange, meaning changes in the digital twin can influence real-world production.
References:
IEEE - Digital Twin: Enabling Technologies, Challenges and Open Research: https://arxiv.org/ftp/arxiv/papers/1911/1911.01276.pdf
IoT Analytics - Digital Twin Market Report 2023-2027: https://iot-analytics.com/product/digital-twin-market-report-2023-2027/
