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Intelligent Testing Fuels Digital Twin Adoption

By Gareth Smith

Gareth Smith

Gareth Smith - 18 July 2019

Digital twins are a growing area of enterprise investment, with Gartner predicting that the use of digital twins will triple by 2022. The technology, a digital representation of a physical object or system, offers companies a host of benefits, among them improved productivity, reduced down time, reduced risk and improved performance.

But realizing these and other benefits hinges on the right technology environment. As Gartner’s Alexander Hoeppe said, “There is an increasing interest and investment in digital twins and their promise is certainly compelling, but creating and maintaining digital twins is not for the faint hearted. However, by structuring and executing digital twin initiatives appropriately, CIOs can address the key challenges they pose.”

One essential consideration for successfully structuring and executing digital twin initiatives is testing. Digital twins’ inherent complexity means that traditional script-based testing is an entirely ineffective approach. Companies must move away from this outdated model and adopt intelligent, AI-driven testing, with auto-generated tests and learning algorithms to determine the pass or fail.

Another reason this modern testing approach is needed is that digital twins are not static and frequently move between software and systems. Let’s take the supply chain sector as an example. By creating a digital twin of a freight truck, companies can have their fleet management, supply chain visibility and vehicle maintenance operating on the digital twin, rather than having to engage constantly with the physical vehicle for these requirements.  

This necessitates that the digital twin move across several companies based upon the various components involved. From a testing perspective, this means that the company must test the compatibility of all the different software systems interacting with the digital twin. As such, in addition to incorporating AI and machine learning, it’s also important that digital twin testing include a test automation tool capable of testing the entire ecosystem.

It’s also vital that companies consider the user experience when testing digital twins. Again, given their complexity and the various organizations and stakeholders typically involved, ensuring performance and usability is critical. Eggplant meets these and additional modern testing requirements through our Digital Automation Intelligence (DAI) Suite, delivering functionality including:  

  • Accessibility testing: The automation of accessibility testing means that organizations can stay compliant and continually improve the user experience
  • Web based low code tooling and auto-generation of all testing assets: This improves ease of use and accelerates the speed of deployment

 The latest version of Eggplant’s DAI Suite also includes capabilities specifically designed to accelerate digital twin adoption. Our new digital twin integration feature enables organizations to easily and interactively define a digital twin of their application using the automated and guided web based graphical interface, and use this to drive intelligent automated testing.

Learn more about these and other features of our DAI Suite and check out this article in IoT Agenda by Eggplant’s Antony Edwards for more on the role of AI-driven testing in digital twin initiatives.