This article explores the growing popularity of digital twins in various industries. While they offer significant benefits such as operational efficiency and cost reduction, challenges like high adoption costs, complexity, and skilled labor shortages hinder their widespread implementation.
A good way to think about digital twins is as a flight simulator for the business. Maneuvers and corrections are made within the safe and relatively inexpensive confines of cyberspace, buttressed by the inflow of the latest data. And, importantly, they are AI models in their own right.
They are growing more popular due to “their ability to provide greater operational efficiency, reduce cost, meet demand for more intelligent data analytics and gain a competitive advantage,”However, there still may need to be more work done to advance digital twinning into many companies. “Digital twins are less widely adopted than you would think, given that they can lead to tremendous efficiency gains,” said, data scientist and IoT industry advisor for SAS.
Just as flight simulators digitally replicate complex aircraft components intermingled with mock events and atmospheric conditions, digital twins were first embraced by “industries with complex products or that rely heavily on automation in their production lines, like aerospace, automotive and the semiconductor industry,” Tutt explained. “Whether for engineering or manufacturing, these industries are using digital twin to model product performance as well as production line performance.
At the same time, “huge data volumes needed to feed the digital twin,” Kügel cautioned. Essentially, “digital twins are machine learning and AI models, embedded in low-code UI interfaces and paired with no-code visualization tools. You need to build digital twins on a stable AI platform that automates the building, training, deployment and maintenance of analytical models.
Digitaltwins Automation Businessefficiency AI Dataanalytics
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