BimScaler Blog – We’ve been hearing a lot about how great digital twins are recently. But have we considered the digital twins challenges?
Because it’s no secret that with big advantages come big challenges.
These challenges cover technical, financial and operational aspects, which often get in the way of them being used more widely.
So yes, we need to talk about the different challenges now. Right, let’s get started.
Challenges of Digital Twins
Let’s take a look at some of the main challenges organisations face when they start on their digital twin adoption.
Initial Setup and Integration
Setting up and integrating digital twins can be tricky because the systems involved are pretty complex and large.
Aidan Fuller and colleagues in “Digital Twin: Enabling Technologies, Challenges and Open Research” say you need to invest a lot in infrastructure to set up a digital twin. This includes advanced sensors, IoT devices and robust data management systems.
The integration process is all about making sure that the data from the physical and digital versions can flow seamlessly between them.
Raphael Wagner et al. in “Challenges and Potentials of Digital Twins and Industry 4.0 in Product Design and Production for High Performance Products” point out how we need to have comprehensive data models and integration frameworks in place to manage the data flow and ensure real-time synchronisation.
What’s more, it can be tough to integrate digital twins with existing legacy systems.
These systems often don’t have the right compatibility for seamless data exchange, so they need a lot of work to make it happen.
This gets even more complicated when you think about the need for continuous updates and synchronisation to make sure the digital twin is always accurate.
Data Quality and Accuracy
Yes, the next challenge is about how reliable digital twins are. And the answer depends a lot on the quality and accuracy of the data it processes.
If the data isn’t up to scratch, it can lead to inaccurate simulations and analyses, which makes the digital twin less effective.
Fuller et al. (2020) highlight the importance of data validation, cleaning and regular updates to keep the data as up to date as possible.
Maria G. Juarez and her colleagues in “Digital Twins: Review and Challenges,” also highlight the difficulty of guaranteeing data accuracy, especially when working with large amounts of data from different sources.
There can be discrepancies and inaccuracies in the data due to sensor errors, data transmission issues and inconsistencies in data formats.
To deal with these problems, we need to have strong data governance in place and use advanced data analytics tools to spot and fix errors as they happen.
Cost and Resource Requirements
It’s not cheap to get digital twins up and running well. The upfront costs, including buying advanced sensors, IoT devices, and data management systems, can be a big hurdle for many organisations.
Wagner et al. (2019) also point out that there are ongoing costs to think about, like system maintenance, data storage and regular updates.
On top of that, digital twins need a lot of human resources to get up and running.
You’ll need skilled people to develop, implement and maintain the systems. This also means hiring data scientists, IoT specialists and engineers, who are often in short supply.
The need for ongoing training to keep up with new tech means there’s more demand on resources.
Security and Data Protection
Security is a huge issue when it comes to digital twins. These systems handle a lot of sensitive data, so they’re a big target for cyberattacks.
As Fuller et al. (2020) point out, we need to make sure we have solid security in place to protect data and stop it from being accessed by people who shouldn’t have it.
This means putting in place advanced encryption, secure access controls and regular security audits.
Maria G. Juarez et al. (2021) also highlight the challenge of making sure that data protection regulations are followed, particularly in industries where privacy requirements are very strict, like healthcare and finance.
If you don’t comply, you could face some pretty serious legal and financial consequences.
That’s why it’s so important for organisations to keep up with the latest regulations and put the right compliance measures in place.
User Adoption and Training
The success of digital twins depends on getting users on board and making sure they’re trained up.
Wagner et al. (2019) say it’s crucial to have comprehensive training programmes in place to make sure employees understand how to use digital twin systems effectively.
This includes training on how to read data, what the system can do, and how to fix any problems.
But getting lots of people to use it can be tricky because people don’t like change.
Some employees might be reluctant to get on board with new tech, especially if they think it’s too complex or disruptive.
To get past this resistance, you need to have good change management strategies in place.
This means making sure everyone knows what the benefits of digital twins are, getting key stakeholders involved in the process, and making sure there’s ongoing support and resources available.
Scalability and Maintenance
One of the main hurdles in rolling out digital twins is making sure they can handle growth and keep running smoothly over time.
As digital twin applications get more complex and produce more data, it gets harder to scale these systems to accommodate new processes, machines, and data sources.
As Fuller et al. (2020) point out, high-performance computing and advanced modelling techniques are key to managing the scalability of digital twins effectively.
Plus, keeping everything up to date involves regular updates to both the physical and digital components to make sure everything’s in sync. This can be pretty resource-intensive and technically demanding.
Interoperability and Integration
Having the right tech in place to make all the different systems work together is important for getting digital twins up and running successfully, especially in complex industrial environments.
Wagner et al. (2019) say that digital twins need to fit in with existing IT and OT systems to give a complete view of how things are running.
This means we need to have standardised data formats, protocols and application programming interfaces (APIs) in place to make sure data can be shared easily and that different platforms can work together.
Hossein Omrany and colleagues in “Digital Twins in the Construction Industry: A Comprehensive Review of Current Implementations, Enabling Technologies, and Future Directions,” highlight the need for semantic data modelling and ontologies to make data integration easier and ensure that different systems can communicate effectively.
Data Storage and Management
The amount of data generated by digital twins is huge, which makes it tricky to store and manage.
If we’re going to use digital twins, we need to make sure we’ve got a solid data infrastructure in place to store, process and retrieve all that data efficiently.
As Fuller et al. (2020) point out, cloud-based solutions and edge computing are often used to manage these large datasets, but making sure the data is accessible and reliable is still a challenge.
Also, it’s essential to manage the data lifecycle, including archiving and deleting data, to prevent data bloat and keep the system running smoothly.
Cybersecurity and Compliance
Given the sensitive nature of the data handled by digital twins, it’s crucial to have robust cybersecurity and compliance measures in place.
Wagner et al. (2019) talk about how we really need to make sure our data is safe from cyber threats by using advanced security measures like encryption and secure access controls.
Sticking to the industry-specific rules and standards is also important to avoid any legal issues and keep people’s trust.
Omrany et al. (2023) say that to tackle these issues, we need to put in place comprehensive data protection policies and do regular security audits.
By the way, if you want to know more about the benefits of digital twins, you can read “From Factory Floors to Hospital Halls: Various Benefits of Digital Twins.”
Future Development and Evolution
The future of digital twins is all about keeping up with new tech and adapting to new developments.
As Fuller et al. (2020) said, digital twins are going to keep on developing as AI, machine learning and IoT tech get better.
This will let us do more sophisticated simulations, predictive analytics and real-time decision-making.
But to stay at the cutting edge, you’ve got to invest a lot in research and development, as well as training for your staff to keep up with the latest tools and methods.
Industry-Specific Challenges
Manufacturing
In manufacturing, digital twins are a huge help when it comes to optimising production processes, improving product design and enabling predictive maintenance.
However, Wagner et al. (2019) point out a few hurdles to integrating digital twins with existing manufacturing systems. It can be tricky because the processes are complex and the data needs to be super precise.
Making sure the data flows in real time and the simulations are spot on calls for advanced sensors and solid data management systems, which can be pricey and technically tricky.
And because manufacturing environments are always changing, digital twins have to be able to adapt to new production lines, equipment, and workflows.
This means you need to have some pretty powerful computers and clever algorithms in place to make sure everything stays in sync between the physical and digital versions.
Healthcare
The healthcare sector sees huge potential in digital twins for personalised medicine, predictive analytics and operational efficiency.
However, as Fuller et al. (2020) pointed out, there are some concerns over data privacy and security that are holding back the integration of digital twins in healthcare.
Patient data is sensitive, and any breach could have serious consequences.
Guaranteeing that we stick to the rules, like the Australian Privacy Principles (APPs), is a big task, but one that we have to get right.
On top of that, the healthcare industry has a tough time standardising data formats and making sure different health IT systems can work together.
This can result in data silos, making it difficult to create a complete and accurate digital twin of a patient’s health status.
Smart Cities
For smart cities, digital twins are a good way to make urban planning, resource management and infrastructure maintenance better.
Omrany et al. (2023) point out a few challenges, including data integration and interoperability.
Cities produce a huge amount of data from all sorts of sources, including IoT devices, sensors and public records.
If we’re going to integrate all this data into one digital twin, we need to have standardised protocols and a solid data governance framework in place.
Another big challenge is making sure digital twin systems can cope with the amount of data they’re dealing with in smart cities.
As cities get bigger and change, digital twins need to be able to handle new data and features.
This means we need to be able to process and analyse large datasets in real time using high-performance computing and advanced analytics.
Aerospace and Defense
In aerospace and defence, digital twins are used to make equipment maintenance, mission planning and operational efficiency better.
The main challenge in this sector, as Fuller et al. (2020) point out, is the need for robust security measures to protect sensitive data.
The data often relates to national security and defence operations, so it’s a major target for cyber attacks.
On top of that, the sheer number of different technologies and platforms that aerospace and defence systems use means that digital twins have to integrate with a lot of them.
Making sure everything works together and stays in sync in such a complex environment is a big technical challenge.
As a starting point for understanding BIM and digital twins, you might want to read “How BIM and Digital Twins Help Your Projects Reach Peak Performance.”
What are the Risks of Digital Twins?
Given all these challenges, we also need to think about the risks digital twins might pose for our projects.
And in this modern age of cyber threats, data security is one of the biggest risks.
As we know, the digital twins handle a lot of sensitive data, so they’re vulnerable to cyber threats.
Having solid cybersecurity measures in place is essential to keep data safe from breaches and unauthorised access.
Another thing to think about is the accuracy and quality of the data. If the data is wrong, it can lead to flawed simulations and wrong predictions, which could cause operational problems and financial losses.
This is especially an issue in sectors like healthcare and aerospace, where precision is really important.
On top of that, getting digital twins up and running can be pricey.
Setting up the initial infrastructure requires a big investment in advanced sensors, IoT devices and computing power.
The ongoing costs, including system updates, data storage and skilled personnel, add to the financial burden.
Finally, there’s the risk of technology becoming obsolete. As digital twin technology develops so fast, systems can quickly become out of date.
To stay on top of new tech, you’ve got to keep investing in R&D and upgrading your systems regularly.
How You Can Get a Digital Twins Support System
As with all new benefits, there are a few initial hurdles to overcome, but nothing insurmountable.
Here at BIM Scaler, we get where you’re coming from and we’re ready to lend a hand. We know you’re facing some challenges, and we’re here to help.
We can help you out with everything CAD and BIM-related, plus we’ll work with you to create the perfect digital twin solution.
We’re here to make digital twins accessible and manageable for a wide range of projects, no matter how big or small.
We’re proud to say we can provide customised solutions, so your digital twin will be perfectly aligned with your project goals.
We’ll take care of all the technical bits and pieces, so your digital twin will fit right in with your existing systems.
This means you can focus on what you do best – managing your core business.
Should you require further information, kindly visit and read our BIM Management Support page.
Or, if you’d like to chat more about this, just drop us a line or give us a call to arrange a lunch meeting. We’d love to show you what we can do for you.
Conclusion
Digital twins are shaking up industries in Australia and the Asia Pacific, opening up new possibilities for innovation and efficiency.
But getting over the hurdles of digital twins takes careful planning, expertise, and tools.
If you can spot these issues early and tackle them head-on, you can make the most of digital twins and gain a competitive edge in the digital world.
It’s not easy to get to grips with the digital twins challenges, but the benefits make it worthwhile.
