The Challenges of Building Digital Twins Compared to Maintaining Simulation Models

Originally conceptualized for aerospace and manufacturing and eventually finding its application in other fields, the concept of simulation has been around for decades, helping industries test scenarios, optimize decisions, and train professionals without risking real-world consequences. Ever since its introduction, it has proven to be a reliable and cost-effective tool for testing and research.

With the development in AI automation came the next step in the evolution of simulation—the digital twins—virtual replicas of physical assets. And with their ability to continuously sync with real-time data, digital twins are transforming how organizations monitor, predict, and manage their systems.

The biggest advantage that virtual twins hold over static traditional simulation models is their dynamic nature, as they are self-learning and evolve with each new piece of data they receive through the continuous connection loop they maintain with their real-life counterparts. This means that at any given point in time, an object, person, or system, and its corresponding digital twin, would be the same. Or at least, that’s how it should be, for that’s what digital twin accuracy is meant to be!

Now, this in itself explains the challenges of building digital twins and why it is significantly harder to maintain them than a traditional static simulation model.

Let’s get to it in detail:

Why Building Digital Twins Is Harder Than Maintaining Simulation Models

1. Real-time Data Integration in Digital Twins: The Biggest Hurdle

Simulation Models

Traditional simulation models run on static datasets that are periodically updated. Once built, they require minimal integration effort—data is imported manually or through scheduled updates, and the model runs independently of external systems.

Digital Twins

Digital twins require continuous data feeds from sensors, EHR systems (in healthcare), machines, IoT devices, or enterprise software. Integrating these data inputs is complex because:

• Devices may produce data in different formats.
• Connectivity must be continuous and reliable.
• Cybersecurity requirements add additional complexity.
• Data ingestion pipelines must handle large volumes at low latency.

Building this real-time data integration in digital twins ecosystem is expensive, time-consuming, and technically demanding.

2. Ensuring High-quality, Interoperable Data

Simulation Models

For traditional simulations, data is pre-processed, cleansed, and treated before being fed to the system, and in case of any data discrepancy, teams can still run hypothetical or estimated scenarios.

Digital Twins

Digital twins run on continuous live data, and for its accuracy this data needs to be clean, accurate, and interoperable. And here lies the challenge because of the following concerns:

• Data silos across platforms
• Inconsistent data types
• Missing or outdated sensor readings
• Legacy systems that don’t communicate
• Standardization issues (especially in healthcare or manufacturing)

As digital twins are dynamic, any data issue directly affects model accuracy, leading to mistrust or incorrect predictions.

3. Dynamic Calibration in Digital Twins vs. Static Updates

Simulation Models

Traditional simulation models don’t need continuous calibrations and only need periodic updating—maybe once every few months or after major system changes. Teams can take time to recalibrate parameters and validate outputs.

Digital Twins

One of the biggest challenges of building digital twins is that they are dynamic systems, and they constantly evolve with every new data they receive.

This means:

• Continuous calibration
• Automated parameter adjustments
• Frequent software and algorithm updates
• Need for advanced machine learning to keep the model aligned with reality

This dynamic nature of digital twins calls for a significant increase in maintenance complexity and engineering effort compared to static simulations.

4. Infrastructure Requirements for Deploying Digital Twins

Simulation Models

Traditional simulations can be maintained with standard computing resources or cloud environments, as they do not require always-on infrastructure.

Digital Twins

Being ‘switched off’ is not an option when it comes to digital twins, as they run on live data feeds, and they need:

• IoT networks
• Edge or cloud computing
• Real-time analytics pipelines
• High-availability infrastructure
• Storage for massive data streams
• APIs and integrations with external systems

Organizations must be ready to invest in a scalable architecture that can handle continuous updates and real-time synchronization.

5. Complexity of Representing Entire Systems

Simulation Models

Traditional simulation model limitations only allow for representation of a single scenario, process, or component. It is not used to capture the full behavior of a system.

Digital Twins

A digital twin often models an entire complex system such as:

• A hospital workflow
• A manufacturing plant
• A supply chain
• The human cardiovascular system and so on…

These require multiscale modeling, domain expertise, and a holistic understanding of physical and digital processes—far more complicated than a single-purpose simulation.

6. Maintenance Is Ongoing and Intensive

Simulation Models

Maintaining a traditional simulation model is relatively within predictable boundaries: fix bugs, update data, tweak variables.

Digital Twins

Digital twins require 24/7 monitoring, including:

• Keeping APIs and device connections alive
• Detecting anomalies in data feeds
• Updating ML models
• Ensuring cybersecurity
• Handling software dependencies and version conflicts

This continuous maintenance footprint makes digital twins far harder and more resource-intensive to sustain.

7. Stronger Security and Compliance Requirements

Simulation Models

Since traditional simulations rely on static, offline datasets which are pre-processed, they are less vulnerable.

Digital Twins

Digital twins are deeply embedded into operations and often deal with sensitive data.

Key risks include:

• Unauthorized access to real-time device streams
• Manipulation of twin data affecting decision-making
• Exposure of personal or operational data
• Increased attack surface due to more connected devices

To guarantee compliance, stay up to date with pertinent rules and consult with legal professionals. Take regulatory standards into account when creating your digital twin’s design.

Conclusion

Surpassing the traditional simulation model limitations, and with their unprecedented ability to provide a virtual space for simulations on a much wider scale, digital twins are revolutionizing industries across the globe. Nevertheless, they demand real-time data integration, scalable infrastructure, interoperability, continuous calibration, strong cybersecurity, and much deeper modeling complexity.

Partnering with expert AI solutions providers like DeepKnit AI can further ease these challenges of building digital twins by automating data extraction, managing heterogeneous data sources, and enabling real-time analytics with high accuracy. Leveraging DeepKnit AI helps organizations handle the complexity, data quality, and maintenance demands intrinsic to digital twins, empowering smarter, faster, and more reliable digital twin accuracy, deployment, and operation.