Entrepreneur, technology evangelist and business strategist. CEO and cofounder of Visartech Inc., a software solution provider.
Digital twin is the current big field of opportunities for businesses. At its heart lies using analyzed data from virtual copies for predicting future business events. This way, decision-making becomes not only more informed but also easier.
However, businesses can’t attain this without addressing the problems associated with digital twin development. Let’s find out what four challenges you may face when building real-time digital twins and explore strategies to effectively tackle them.
Transforming 2D To 3D Model
Digital twins rely on 3D representation to provide a realistic visualization and precise simulation. However, the primary challenge lies in the conversion process. The transformation from a 2D model to its 3D counterpart demands a profound understanding of spatial dimensions, precision and visual accuracy.
But how can this challenge be effectively addressed?
Importing CAD Files Into 3D
Computer-Aided Design (CAD) files are rich in spatial data, making them the ideal foundation for generating precise 3D models. Specialized software can extract and convert this data into a 3D format, ensuring not only accuracy but also expediting the entire process.
This approach seamlessly integrates 3D models into digital twin systems, thereby elevating their visualization and simulation capabilities.
Applying 3D Point Cloud Modeling
Another approach involves the application of 3D point clouds which are collections of points in three-dimensional space that show the object’s form and arrangement. You scan the object and record data points from a 3D model of an object.
Once a 3D structure is established, you can refine the model—simulate real-world scenarios, test structural integrity, or craft immersive virtual environments.
Compatibility With The Company’s Ecosystem
The new digital twin means new platforms and technologies in the company’s infrastructure. The thing is, if they aren’t seamlessly integrated with the tech components already in place, it can lead to both time and cost-consuming adaptation of a new solution.
Use the following way to overcome this challenge:
Integration With Enterprise Resource Planning System
By integrating with ERP, you guarantee smooth data sharing between the virtual twin and existing company systems. This ensures that information gathered and analyzed by the digital twin is automatically reflected in the ERP system, and vice versa.
With this information flow, your digital twin will work with other business processes easier. The results are savings in time and resources when implementing a digital twin. Also, it maintains data unity and consistency across the company, which is crucial for making management decisions based on reliable information.
Data Structure & Quality
The core of this challenge is the fact that field data doesn’t follow a single standard and can be low quality for working with them. This is because data integration platforms from different providers adhere to distinct standards and approaches when displaying information. Also, the lack of a common database introduces additional complexity to the process.
Here’s how to solve it:
Data Extraction And Transformation
This is the process of extracting data from external sources and then transforming it for storage in an appropriate structure or format. Typically, the first step is time-consuming. So to minimize the overall process duration, it’s common to perform both data extraction and transformation at the same time.
Such an approach optimizes the merging of data into one analytical model, which helps to present information in an easily accessible format. As a result, this makes it simpler for the digital twin to read the gathered data.
Digital Twin Scalability
Scaling involves the capacity to enlarge a project while maintaining its efficiency and functionality. This requires considering increased system loads with expanding users and data volume, as well as resource management and optimization to ensure performance and reliability at ever-expanding scales.
Consider this approach for addressing this problem:
Deploy Your Twin In The Cloud
Deploying the application in cloud services facilitates digital twin scaling by providing flexibility and ease in resource usage. In a cloud environment, you can increase or decrease computing power and data storage depending on the load.
Once deployment is done right, the system will quickly respond to changes in the volume of users and data. There’s no need to invest in the maintenance of your own hardware in that case. What’s more, cloud services give tools for automatic resource monitoring and management to ensure system stability when the load increases.
When it comes to understanding how beneficial digital twins are for making business decisions, companies should keep in mind that it’s possible only with the right development approach. After all, without taking into account such a factor as data management, there is a risk of receiving outdated information about the system or process that the digital twin mirrors. By addressing the challenges mentioned above, you can be sure that your digital twin development project is on the right track and maximizes its potential advantages.
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