Digital Twins in Industrial Automation

By Dustin Guttadauro, Product Line Manager - Telecom & Fiber, Infinite Electronics  

Digital twins have become one of the most discussed technologies in Industry 4.0, but the concept is often misunderstood. While many people associate digital twins with 3D models or simulations, a true digital twin is a live, continuously updated representation of a physical asset or process. By combining sensor data, industrial networking, computing platforms, and analytics software, digital twins help manufacturers improve visibility, optimize operations, and make more informed decisions. Understanding how digital twins work—and the infrastructure required to support them—is critical for organizations evaluating smart manufacturing initiatives.  

What is a digital twin in industrial automation? 

A digital twin is a continuously updated virtual model of a physical asset, system, or process. It isn't a static CAD model or a periodic simulation. It's a live representation that changes as the physical object changes, fed by sensor data, updated in near real time, and used to monitor, analyze, and predict what the physical asset will do next. 

In industrial automation, a digital twin might represent a single CNC machine, an entire assembly line, a compressor in a gas plant, or a full manufacturing facility. The scope depends on the use case. A maintenance team focused on equipment reliability might twin individual assets. An operations team optimizing throughput might twin an entire production cell. 

The term was coined by Michael Grieves at the University of Michigan in 2002 and formalized in NASA's 2010 technology roadmap for aircraft lifecycle management. It's since become central to Industry 4.0 strategy, but its practical implementation still runs ahead of the industry's ability to execute it well, mostly because the physical data infrastructure required is harder to get right than the software layer on top. 

How is a digital twin different from a simulation or a 3D model? 

This is where the marketing language gets sloppy and it matters to get it right. 

The distinction matters because many vendors sell 'simulation' or 'virtual model' capabilities as digital twins. A model that isn't fed by real-time operational data is not a digital twin. It's a design tool. Useful, but different. 

What are the main use cases for digital twins in manufacturing and automation? 

Five applications account for the majority of industrial digital twin deployments. 

Predictive maintenance 

A digital twin of a motor, pump, or gearbox monitors vibration signatures, temperature profiles, current draw, and operating hours. When the pattern of readings deviates from the established baseline, the twin flags an anomaly before the physical asset fails. The maintenance team schedules a repair during planned downtime instead of responding to an unplanned failure mid-run. 

Connectivity requirement: Vibration and temperature sensors with fast sample rates (typically 1 kHz or higher for vibration), reliable wireless or wired gateways, and edge processing to filter noise before the data reaches the twin. 

Process optimization 

A digital twin of a production line or process cell runs continuously alongside the physical line, comparing actual outputs with predicted outputs. When yield drops or cycle time extends, the twin isolates which variable changed. Engineers adjust setpoints on the twin first, confirm the predicted outcome, then apply the change to the live process. Connectivity requirement: Dense sensor coverage across the cell for temperature, pressure, flow, position, and power with sub-second update rates to capture the dynamic behavior the twin needs to model accurately. 

Remote monitoring and operations 

For facilities that are geographically distributed or difficult to access – offshore platforms, remote compressor stations, and multi-site manufacturing networks – digital twins let operations centres monitor and diagnose assets without boots on the ground. An operator in Houston can watch a compressor twin in real time and catch a developing problem before it becomes an evacuation. Connectivity requirement: Industrial-grade cellular orindustrial wireless gateways with redundant uplinks, because a remote monitoring use case has no fallback if connectivity fails. 

Workforce training and simulation 

A digital twin of a machine or production cell gives new operators a realistic training environment without putting production equipment at risk. The trainee interacts with a virtual machine that behaves exactly as the physical one does — same responses, same failure modes, same edge cases. Training ROI compounds as the twin is updated to reflect the current machine configuration rather than the factory-floor state from five years ago. 

New product and process development 

Before a new product runs on the physical line, engineers can simulate its behavior in the digital twin — testing toolpaths, fixture designs, and process parameters. Defects discovered in simulation cost a fraction of what they cost to discover during a live production run. This is the use case that attracts the most C-suite attention because the ROI appears on the product development P&L before the asset ever runs a single unit.  

What infrastructure does a digital twin actually need? 

This is the part most digital twin articles skip. The software platforms get covered in depth – PTC ThingWorx, Siemens Xcelerator, ANSYS Twin Builder, GE Predix, and Azure Digital Twins. The physical infrastructure that feeds them gets a footnote, if that. A digital twin is only as accurate as the data it receives. Getting that data from a physical asset to the software platform requires four layers, and failures at the lower layers are the most common reason digital twin projects underdeliver. 

Layer 1: Physical sensing 

Industrial sensors are the data source. Temperature sensors, vibration accelerometers, pressure transducers, flow meters, current transformers, proximity sensors, and vision systems all contribute different data streams to the twin. The sensors must be specified for the environment — IP67 or higher for wet or dusty conditions, ATEX-rated for hazardous atmospheres, and extended temperature range for process environments near heat sources. Browse L-com'sindustrial IoT sensors for environment-specific options. 

Sensor selection mistakes at this layer — wrong IP rating, insufficient sampling rate, poor placement — produce data that looks valid but misrepresents what the asset is actually doing. A digital twin trained or calibrated on flawed sensor data will make predictions that are confidently wrong. 

Layer 2: Industrial networking 

Sensor data has to move. Industrial networks — wired Ethernet, industrial Wi-Fi, fiber optic backbone, or cellular — carry data from the shop floor to edge devices and upstream platforms. The network has to handle factory-floor conditions: electromagnetic interference from variable-frequency drives and servo motors, physical environments that aren't cable-friendly, and latency requirements that vary by application. 

Wireless gateways aggregate data from multiple sensors and forward it upstream. For predictive maintenance applications, update latency needs to be low enough to catch fast-developing failures — a gateway that batches data every 60 seconds will miss a bearing that fails in two minutes. L-com'sindustrial wireless gateways support MQTT, OPC-UA, and Modbus for protocol-flexible deployments in environments where running cable is impractical. 

For backbone connections between cells, buildings, or control rooms,fiber optic connectivity is the right choice. Fiber is immune to EMI, supports the bandwidth required for high-density sensor environments, and eliminates the ground loop problems that plague copper runs in industrial settings. 

Layer 3: Edge and cloud processing 

Edge computing processes data close to the source, filtering noise, normalizing formats, and running time-series compression — before forwarding upstream. This reduces the bandwidth required for cloud transmission, lowers latency for applications that need fast response, and keeps sensitive operational data within the facility boundary. For safety-critical applications, edge processing is often required by the application architecture: a response that has to travel to the cloud and back may be too slow. 

Cloud platforms handle long-term storage, cross-site aggregation, and the compute-intensive model training that digital twin software relies on. The right architecture distributes processing based on latency requirements: edge for fast-loop control, cloud for slow-loop analytics. 

Layer 4: Digital twin software 

The software platform creates and maintains the virtual model, ingests the data streams, and runs the analytics that generate value. Major platforms include Siemens Xcelerator, PTC ThingWorx, ANSYS Twin Builder, GE Predix, Microsoft Azure Digital Twins, and AWS IoT TwinMaker. Each has different strengths — simulation fidelity, integration depth, and domain focus — and the right choice depends on the use case and existing technology stack. The software layer is not where most digital twin projects fail. It's the most mature part of the stack. Most failures trace back to data quality problems at Layer 1 or connectivity reliability problems at Layer 2. 

What does it actually cost to build a digital twin? 

Cost ranges are wide because scope ranges are wide. A single-asset digital twin for predictive maintenance on one production-critical machine — sensors, gateway, edge node, and software subscription — can be deployed for under $50,000. A facility-wide digital twin covering hundreds of assets, integrated with MES and ERP, delivering real-time process optimization, runs into the millions. 

The more useful framing is cost per asset instrumented. Sensor hardware cost has dropped sharply over the past decade — an industrial-grade sensor that cost $800 in 2014 is $150–$200 today. The cost that hasn't dropped as fast is integration: getting data from sensors into a form the twin software can use, with the right protocols, at the right update rate, reliably. That's where experienced systems integrators add value.  

How long does it take to build a digital twin? 

For a single asset with good existing sensor coverage: four to eight weeks from kickoff to operational twin. For a production line with mixed instrumentation levels: three to six months. For a facility-wide deployment: twelve to twenty-four months, usually executed in phases. 

The schedule is determined by instrumentation readiness more than software. Facilities that have already deployed sensor infrastructure and established data pipelines can stand up a digital twin quickly once the software platform is selected. Facilities that need to run new sensors, pull cable, configure gateways, and validate data quality before the software can be deployed take proportionally longer. 

This is why the investment sequence matters. Manufacturers who have been building out their IIoT infrastructure incrementally — adding sensors, upgrading gateways, and running fibre are ready to deploy digital twin software the moment the business case is approved. Those starting from a bare floor face a much longer runway. 

How do you build a digital twin? A step-by-step checklist 

The right sequence is infrastructure first, software second. Here's a practical build order: 

 1.  Define the asset and use case. What physical asset are you twinning, and what decisions will the twin support? Predictive maintenance, process optimization, and remote monitoring have different data requirements. Start specific. 

2.    Audit existing sensor coverage. Map every measurement point on the target asset. Identify gaps — variables the twin needs that aren't currently instrumented. 

3.    Specify sensors for the environment. Match sensor specifications to operating conditions: IP rating, temperature range, vibration tolerance, and sample rate. Under-specified sensors in harsh environments are the single most common source of digital twin data quality failures. 

4.    Design the connectivity architecture. Determine how sensor data reaches the twin platform. Wired vs wireless, protocol choices, gateway placement, and edge processing requirements. Design for the update rate the use case requires, not just what's easy to install. 

5.    Build and validate the data pipeline. Understand the data flow before selecting twin software. Confirm data arrives at the expected rate, without packet loss, in the correct format. Fix data quality issues at the source, not in software. 

6.    Select the twin software platform. Evaluate platforms against integration requirements, the existing technology stack, and use-case fit. Run a proof-of-concept on the live data pipeline before committing to a platform. 

7.    Build and calibrate the initial model. Create the virtual model of the asset, calibrate it against known operating conditions, and validate that model outputs match observed physical behavior. 

8.    Deploy and iterate. Run the twin in parallel with the physical asset. Track prediction accuracy. As the model accumulates operating history, refine it. Digital twins improve with data — the first version is never the best version. 

Building Resilient Industrial Networks 

Reliable industrial security depends on more than firewall rules and network segmentation. The underlying physical infrastructure must be designed to support continuous operation in demanding environments. From industrial Ethernet and fiber connectivity to wireless networking and ruggedized connectivity solutions, L-com helps organizations build resilient industrial networks that support security, reliability, and long-term operational performance. 

Frequently Asked Questions (FAQs) 

What is a digital twin in industrial automation? 
A digital twin is a continuously updated virtual model of a physical asset, process, or system. Unlike a static model or simulation, it uses real-time sensor data to reflect current operating conditions and support monitoring, analysis, and optimization. 

How is a digital twin different from a simulation? 
A simulation models how a system is expected to behave under specific conditions. A digital twin continuously updates using live operational data, allowing it to reflect actual performance, wear, and changing conditions in real time. 

What infrastructure is required to build a digital twin? 
A digital twin requires four foundational layers: sensors to collect data, industrial networking to move data, edge or cloud computing to process information, and software to create and maintain the virtual model. 

What are the most common uses for digital twins in manufacturing? 
Manufacturers use digital twins for predictive maintenance, process optimization, remote monitoring, workforce training, and new product development. The specific implementation depends on the operational goals and available data sources.