9 Key Considerations for Digital Twins in Manufacturing
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Many manufacturers are ahead of the curve when it comes to digital 4.0, but not all may know about the numerous potential benefits of digital twins. A virtual replica of a physical product, asset or system, a digital twin makes the physical computable. It offers manufacturers a range of advantages, including better business visibility, increased product reliability and new revenue streams.
Is digital twinning right for your business? Below are some key considerations to weigh as you think about adopting this advanced manufacturing technology.
- Digital twins are not complete representations of a product.
Digital twins are akin to algorithms. They are highly reliant on data input, and since it’s nearly impossible to turn every aspect of a physical product into data, digital twins are not precisely twins, though they are pretty close. A digital twin is created by outfitting a product with sensors that can track functionality. These can then be used to study simulations of the product’s performance. So digital twins are made up of models and data, but their complexity is reliant on the data used to create them.
- Digital twins evolve over time.
As a product moves through its lifecycle, the information in its digital twin will shift in response to its performance, technical configurations and environmental parameters.
- Information and data are key across a product’s lifecycle.
For a digital twin to remain relevant and useful over time, make sure you are utilizing a data structure that can be easily used and exchanged over different systems and applications.
- You can use digital threads to enable digital twins.
Digital threads are a communication framework that link all elements of a product’s data, from design to obsolescence. Using them reduces the complexity of digital-twin implementation and increases digital twins’ accuracy.
- Transparency is critical.
Identify, classify and correlate data across various sources so there’s transparency and automated information-identification processing. These are crucial for smooth digital-twin deployment.
- Open format is best.
In contrast to a proprietary system, which ties an organization’s data to specific systems, limiting its use, an open format ensures that your digital twins can be easily updated, scaled and extended when new models and data representing new outcomes become available.
- Your device management plan matters.
In addition to ensuring that data is in a format that can be accessed and used over time, you should make similar considerations for devices that will access that data (i.e., phones, tablets and laptops). Make sure that your device plan can keep up with your needs for monitoring, updating and security.
- The cloud is your friend.
Cloud-based computing, storage, analytics and artificial intelligence/machine learning services enable operational technology and information technology managers to build, deploy and grow solutions quickly and affordably.
- There are costs and benefits.
Digital twins today may be expensive to build and maintain, but they enable technical agility and speed that foster easier scaling—and save money in the long run to boot.
Learn more about digital twins: As decision-makers in manufacturing embrace digital transformation, it is imperative to consider digital twins as key pieces of the process. For more insights on digital twins in manufacturing, read Digital Twins: The Key to Unlocking Value and Innovation.