Leveraging Digital Twins To Tactically Avoid Disruption

When it comes to manufacturing and product development, the earlier on a design flaw is spotted, the more money likely to be saved – in addition to upfront costs associated with addressing design flaws, the business ramifications of having a customer be the first to identify a product’s issues could be disastrous.

Enter simulation, which empowers manufacturers to better ensure correct design functionality and to explore scenarios that might lead to failure before a physical product is built.

While simulators that model new products and processes have become a core component of new product development, they aren’t new.

 In the late 1980s, simulators were used extensively to model new computers. Event-driven simulators that emulated the functionality of new computer processor designs were developed without most modern technologies, such as cloud computing and threaded processing. 

Today’s simulators, commonly referred to as “digital twins,” are much more advanced than those developed several decades ago – they’re much more useful, too.

Design Validation and Performance Evaluation

Today’s digital twins serve two primary functions: to support design validation and enhance performance evaluation within the context of theoretical specifications.

Leveraging digital twins in conjunction with other emerging technologies, such as Artificial Intelligence, can help provide manufacturers with strategic insights into new opportunities and threats, enabling faster time to market and other competitive advantages.

Originally, digital twins were primarily utilized to find faults/defects within product designs – today, digital twins enable a plug and play (PnP) approach to allow manufacturers to cost-effectively explore alternative solutions to specific designs, potential improvements and the impact of a change on the performance and cost of a product.

Digital twins are modular in that they have the capacity to accommodate low-level changes and/or large-scale modifications, such as entirely substituting in a higher-level model for a newly proposed design that hasn’t even been implemented yet.

This offers companies a powerful competitive advantage, particularly as new technologies become available. Enterprises need to grow as technology evolves and not rebuild new digital twins from the ground up to include new system components or to accommodate vendor design changes. Having the ability to implement changes and rework designs quickly and confidently provides enterprises with the opportunity to grow more sustainability and efficiently.

Furthermore, for most enterprises, digital twins aren’t just used to model physical devices – they can also integrate models of the entire supply chain, the manufacturing process, marketing and customer behavior models, and more, which enables enterprises to play out high-risk scenarios and to better preemptively identify stress points throughout their entire business model.

Moving forward, more companies will find value in using digital twins to model alternatives and associated risks in anticipation of disruptions in the supply or distribution chains. Digital twins will cost-effectively help them find innovative ways to survive and thrive.