Demystifying common misconceptions about digital twins 

While digital twins have become widely known and adopted into organisations looking to enhance their monitoring, analysis and optimisation capabilities, the term “digital twin” is often misunderstood or oversimplified, leading to confusion about its true value and application. As a virtual representation of a physical entity (such as an object or system), understanding its function and capabilities within an organisation can feel challenging. With the right tools and understanding, however, digital twins can bring tremendous value.  

So, what are the common misconceptions arising about digital twins that organisations should be aware of to understand the true value they can bring to operations?  

What common misconceptions arise with digital twins?

“Creating a digital twin is a one-time effort. ”

• Misconception: Some assume that once a digital twin is created, it doesn’t require further development or updates.  
• Reality: Digital twins need ongoing lifecycle management as they evolve with the real-world entities they represent. Continuous data integration and model refinement are essential for their effectiveness.  

“Digital twins are only useful for large organisations or complex systems.”

• Misconception: There’s a belief that only large enterprises or those with highly complex systems can benefit from digital twins.  
• Reality: Digital twins can be valuable for organisations of all sizes and complexities. Even small businesses can benefit from simpler digital twin implementations that provide valuable insights.  

“A digital twin must be a perfect replica of its physical counterpart. ”

• Misconception: Some believe that a digital twin needs to exactly mirror every detail of its physical counterpart.  
• Reality: While accuracy is important, a digital twin is often a simplified or abstracted model that focuses on the most relevant aspects to achieve the desired outcomes. It doesn’t need to replicate every detail.  

“Digital twins require advanced AI or ML to be effective.”

• Misconception: There’s a common assumption that advanced AI or ML is necessary for a digital twin to provide value.  
• Reality: While AI and ML can significantly enhance a digital twin’s capabilities, many effective digital twins rely on simpler data analysis, rule-based systems and straightforward simulations.  

“Implementing a digital twin is prohibitively expensive and time-consuming. ”

• Misconception: Many organisations are deterred by the belief that digital twins require huge investments of time and money.  
• Reality: The cost and time investment vary depending on the complexity of the Digital twin. There are scalable solutions and incremental approaches that allow organisations to start small and expand their digital twin capabilities over time.  

“Digital twins can solve all operational problems.”

• Misconception: There’s an overestimation of what digital twins can achieve, with some believing they are a panacea for all operational issues.  
• Reality: While digital twins are powerful tools, their effectiveness depends on accurate data, proper implementation, and integration with broader business strategies. They are not a cure-all but rather a part of a larger toolkit for operational improvement.  

Get in touch with our Mood experts today to explore how digital twins can benefit your business, or contact us for more information.