Industrial Digital Twins: How Manufacturers Can Test Operational Changes Before Implementation

A small change in a manufacturing process can affect several parts of a facility. Moving one machine, changing a production schedule, or introducing new equipment may influence material flow, energy consumption, and production time.

Industrial digital twins offer a way to study these effects before making changes to physical operations. By representing equipment and processes digitally, organizations can compare possible scenarios, identify potential problems, and make better-informed engineering decisions.

What Is an Industrial Digital Twin?

An industrial digital twin is a digital representation of a physical asset, system, or process. Depending on its purpose, it may combine engineering models, operational records, sensor data, and simulation software.

A simple 3D model shows the appearance of an object. A digital twin can go further by representing how an asset or process behaves under different operating conditions. Some digital twins also receive updated data from connected equipment, helping them reflect changes in the physical environment.

For example, a manufacturer could create a digital representation of a production line to study how machines, material movement, and production schedules interact.

The usefulness of the model depends on its accuracy, the quality of its data, and how well it represents the real system.

1. Evaluating Production-Line Changes

Manufacturers regularly consider changes to equipment layouts, production capacity, and workflow design. Testing every possible arrangement in a working factory may require additional time and resources.

A digital twin can help engineers compare alternative arrangements before selecting one for physical testing.

Consider a factory planning to install an additional machine. Engineers need to determine whether the new equipment will improve output or create delays elsewhere in the production line.

A simulation could compare the existing layout with a proposed arrangement. The team could examine machine utilization, waiting times, and material movement to identify possible bottlenecks.

The results would not guarantee that the new layout performs as predicted. However, they could help engineers identify risks and decide which changes deserve further testing.

2. Finding Hidden Process Inefficiencies

Operational inefficiencies are not always caused by the equipment experiencing the most visible delays.

A machine may remain idle because materials arrive late. A packaging station may become overloaded because an earlier process releases products faster than it can handle them.

These problems are connected, making them difficult to understand by examining individual machines alone.

A digital twin can represent relationships between different stages of a process. Engineers can then investigate how changes in one area affect the rest of the system.

For example, they might compare different production schedules to determine whether adjusting the timing of one stage could reduce waiting time across the facility.

This approach is especially useful when production depends on several interconnected processes.

3. Supporting Energy and Resource Planning

Energy consumption, material waste, and equipment utilization can influence manufacturing costs.

Digital twins can help organizations investigate how different operating conditions may affect resource use. A model might compare production schedules, equipment settings, or process configurations to estimate their potential impact.

For instance, a facility could evaluate whether shifting selected operations to different periods would reduce peak energy demand without creating production delays.

Such estimates need to be compared with actual measurements before implementation. The reliability of the findings depends on the model's assumptions, the available data, and the operating conditions being represented.

Digital twins should therefore support engineering analysis rather than replace real-world verification.

4. Combining Digital Twins With AIoT

The Artificial Intelligence of Things (AIoT) combines connected devices with artificial intelligence. Sensors collect information from physical environments, while AI systems analyze the data to identify patterns or unusual conditions.

When integrated with a digital twin, these capabilities can help organizations compare current operating conditions with alternative scenarios.

For example, sensor data may indicate that a production process is taking longer than expected. A digital twin could help engineers investigate how different equipment settings or workflow adjustments might affect the wider process.

The combination can be valuable when timely operational data and accurate models are available. However, it requires suitable data integration, reliable systems, and careful validation.

Not every digital twin needs AI or continuous sensor updates. The appropriate design depends on the problem the organization wants to solve.

5. Challenges to Consider Before Implementation

Developing a digital twin requires more than creating a visual model of a factory.

Data quality: Inaccurate or incomplete information can produce misleading results.

System integration: Different machines and software platforms may use incompatible data formats or communication methods.

Model accuracy: A simplified representation may overlook important physical constraints or operating conditions.

Cybersecurity: Connected systems require appropriate access controls, secure data handling, and regular maintenance.

Cost and expertise: Organizations need to consider development expenses, technical skills, ongoing updates, and the resources required to maintain the model.

Businesses should begin with a clearly defined operational problem rather than attempting to represent an entire facility immediately.

6. How to Measure Whether a Digital Twin Is Useful

A practical implementation starts with a specific question. For example, a manufacturer might want to reduce waiting time between production stages or evaluate whether a proposed equipment layout is feasible.

The team can then follow a structured process:

1. Define the problem. Identify the operational issue and the decision the model should support.
2. Choose measurable indicators. These might include cycle time, machine utilization, waiting time, energy consumption, or production throughput.
3. Collect relevant data. Use available equipment records, process measurements, and engineering specifications.
4. Build a focused model. Represent only the assets and processes necessary to investigate the problem.
5. Compare scenarios. Evaluate realistic alternatives and document the assumptions behind each one.
6. Validate the findings. Compare model predictions with observed results and conduct physical tests before making significant changes.

This process helps organizations determine whether the digital twin provides useful information and whether further investment is justified.

Conclusion

Industrial digital twins can help manufacturers investigate operational problems, compare engineering alternatives, and understand how changes may affect connected processes.

Their value does not come from digital representation alone. It depends on asking the right questions, using reliable data, selecting appropriate models, and validating results against real operating conditions.

For industrial organizations, starting with one measurable problem can be a practical way to explore the technology. A focused, well-tested digital twin may provide more useful insights than a complex model built without a clear operational objective.