Interscale Content Hub – It’s no secret why digital twins are making waves in so many industries. But we also need to be aware of the digital twins challenges.
These challenges, including data privacy concerns, integration difficulties, and the need for skilled personnel, must be carefully navigated to fully harness the potential of digital twin technology.
So, let’s take a look at what we need to get over first.
Major Challenges of Digital Twins
Data Privacy and Security
The biggest worry when it comes to digital twins is data privacy and security. These systems need to handle a lot of sensitive data from different sources, so it’s important to make sure that it’s all kept safe.
Honghai Wu, Pengwei Ji, Huahong Ma, and Ling Xing, in “A Comprehensive Review of Digital Twin from the Perspective of Total Process: Data, Models, Networks, and Applications,” point out how vital to make sure the data collected, stored, and analyzed in digital twins is kept safe from breaches and unauthorized access.
They strongly advocate for robust cybersecurity measures, such as encryption, access controls, and regular security audits, to keep sensitive information safe.
Similarly, Diego M. Botín-Sanabria and colleagues’ research in “Digital Twin Technology Challenges and Applications: A Comprehensive Review“ also looked at the challenges and applications of digital twins.
Consequently, it makes it clear that we need to ensure the security of data shared across different platforms and stakeholders.
Botín-Sanabria proposes a blockchain-based strategy to manage data security, ensuring that data transactions are secure and traceable.
This method also addresses the issue of data integrity, which is crucial for maintaining trust in the digital twin systems.
Integration with Legacy Systems
We’re facing a big challenge when it comes to integrating digital twins with existing legacy systems. Many industries have outdated infrastructure, which makes it tough to make the switch.
Honghai Wu and his colleagues talk about the difficulty of integrating advanced digital twin technologies with legacy systems that weren’t designed to support such integrations.
They suggest a gradual approach to integration, where digital twins are gradually incorporated into the existing infrastructure, ensuring minimal disruption to ongoing operations.
Wu and his colleagues also say it’s important to understand both the new technology and the legacy systems to make sure the integration goes smoothly.
This means investing a lot of time and resources to adapt the existing systems to accommodate the new technology.
High Initial Costs and Investment
The upfront costs of digital twin technology can be pretty steep. It can cost a pretty penny to get the hardware, software, and skilled personnel in place.
The development and maintenance of digital twins require a lot of investment, which can be a barrier for many organizations, especially small and medium-sized businesses.
So, you need to do a cost-benefit analysis to determine the long-term benefits and look for funding or partnerships to help with the initial costs.
Diego M. Botín-Sanab and his colleagues also discuss this issue and argue how strategic investments in digital twin technology are important.
Botín-Sanab says companies should focus their investments on the areas that will give them the biggest return on their investment, which makes the upfront costs seem more reasonable.
We’ve put together a list of the advantages of DTs, which you can read in “Slashing Costs: The Benefits of Digital Twins (and How to Get Started).”
Complexity in Implementation
Bringing digital twins to life can be pretty complex, with lots to think about like connecting IoT sensors, data analytics, and machine learning algorithms.
Wu, Ji, Ma, and Xing say you need to get everyone on the same page with a solid plan and clear coordination across different departments to make it work.
They suggest creating a structured plan with clear milestones and responsibilities to manage this complexity effectively.
Diego M. Botín-Sanabria and his colleagues agree. They say that the complexity of digital twins comes from the need to integrate lots of different technologies together.
They suggest keeping an eye on the implementation process and making adjustments as needed to address any new challenges and keep the system running smoothly.
Data Management and Quality
Manage your data well and make sure it’s of high quality if you want your digital twins to succeed.
If the data isn’t up to scratch, you’ll end up with an inaccurate model, which means the digital twin won’t work properly.
Wu et al. (2023) make a great point: You’ve got to have rock-solid data governance frameworks in place to make sure your data is accurate, consistent, and complete.
They also say it’s essential to regularly audit and update data management practices to keep data quality up over time.
Botín-Sanabria et al. (2002) also talks about the challenges of managing all the data that digital twin systems generate.
Botín-Sanabria et al. suggest using advanced data management tools and techniques to handle the volume and complexity of the data, so it’s reliable and useful for decision-making.
Need for Skilled Workforce
A talented team is key to developing and maintaining digital twins.
But there just aren’t enough professionals with the right skills in areas like data analytics, machine learning, and IoT.
Wu and colleagues say training and development programs are the way to go if you want to build a skilled workforce that can handle digital twin technology.
They also suggest teaming up with academic institutions and offering internships to fill the skills gap.
Botín-Sanabria and his colleagues make a similar point, saying you need to learn and develop continuously to keep up with rapidly evolving technology.
They say organizations should create a culture of innovation and continuous improvement to attract and keep skilled professionals.
Real-Time Data Processing
Real-time data processing is key to making digital twins work, but it also brings its own set of challenges.
In their paper, Wu, Ji, Ma, and Xing talk about the need for more advanced computational resources and more efficient data processing pipelines to handle real-time data.
They suggest investing in high-performance computing infrastructure and developing algorithms that are optimized for real-time analysis.
Botín-Sanabria and his colleagues also talk about how important it is to process data in real time. They say that if you wait too long to process data, you might end up with an outdated or inaccurate model.
Botín-Sanabria and his colleagues say we should use edge computing and other advanced tech to cut down on latency and make sure data is processed on time.
Industry-Specific Challenges
Aside from the general challenges we’ve already talked about, each industry has its own specific hurdles to overcome when it comes to digital twin adoption.
For instance, in the automotive industry, ensuring the accuracy and reliability of simulations is critical.
According to Botín-Sanabria et al. (2022), modeling and simulating automotive systems is complicated by the need to integrate physical and virtual components accurately.
In healthcare, patient privacy and data security are really important. Botín-Sanabria et al. says how managing sensitive health data while keeping everything up to date and making sure everything’s in line with the rules is a big challenge.
Smart cities bring another set of challenges, as Botín-Sanabria et al. points out.
Integrating digital twins in urban planning means handling lots of data from different sources, like traffic systems, public utilities, and environmental sensors.
We’ve got to make sure these systems can work together seamlessly while keeping data accurate and secure.
In manufacturing, the challenge is integrating digital twins with existing production systems.
Many manufacturing facilities still use legacy systems that aren’t designed to support advanced digital twin technologies, according to Botín-Sanabria et al.
Upgrading these systems to make room for digital twins can be pricey and tricky.
Another useful reference on BIM and DTs is “BIM and Digital Twins = Construction’s Dream Team: Are You on Board?“
Strategies to Overcome Digital Twin Challenges
If you’re facing challenges with digital twins, there are a few strategies you can try to overcome them.
Botín-Sanabria et al. suggest creating a clear plan for implementing digital twins, with goals, milestones, and responsibilities clearly defined.
The roadmap should lay out the steps for integrating digital twin technology with existing systems, flag any potential bottlenecks, and suggest ways to overcome them.
Another great strategy is to invest in training and development programs to build a skilled workforce.
We have to be mindful of the value of continuous learning and development to keep up with rapidly evolving technology.
Partnering with academic institutions and offering internships can also help bridge the skill gap and make sure that employees have the necessary expertise to manage and maintain digital twin systems.
Protecting sensitive data is a top priority, so enhancing cybersecurity measures is important.
Botín-Sanabria et al. say we should implement solid cybersecurity protocols, including encryption, access controls, and regular security audits.
Compliance with data protection regulations is also key to maintaining trust and safeguarding sensitive information.
Taking a step-by-step approach to integrating the new technology can help make it easier to manage.
Your organization can keep its operations running smoothly by gradually integrating digital twin technology into existing systems.
Then, partnering with tech companies and universities can give you access to the know-how and resources you need.
Botín-Sanabria et al. discuss the benefits of working with other companies to develop and use digital twin solutions.
These partnerships can help companies get the latest knowledge, access cutting-edge technology, and benefit from the latest research and developments in the field.
That’s why Interscale offers reliable solutions for managing Building Information Modeling (BIM) and digital twins for your projects.
Our team of experts is ready to help your team with any support or training they need to implement and manage BIM and digital twins successfully.
As a starter, kindly click our Interscale BIM Management Support Service here to learn more about how we can help you harness the power of BIM and digital twins.
We can also set up a one-on-one meeting to discuss your specific needs and how we can help your project succeed.
In Closing
Digital twins have the potential to transform industries, but there are a few hurdles to overcome before they’re fully adopted.
Working with experienced partners like Interscale can also be a great way to get the guidance and resources you need to get through the complexities of digital twin implementation.
With Interscale, you’ll be able to tackle digital twins challenges and make sure your company gets the hang of adopting new technology.
