Most machines in the industry today already work with a certain level of digitalization. But the current systems are only for day-to-day working and sustenance. The upcoming digital twin models are much more powerful: they can disassemble the product and project a realistic representation of its parts. Secondly, the virtual representation reacts realistically to testing. And the third is utilizing information from the virtual product through physical inspection. Such coupling of the digital and physical worlds allows data analysis, systems monitoring, and simulation of real-world settings to proactively plan the details, prevent downtime, improve operations, and create new revenue models. Overall, digital twin technology covers the end-to-end lifecycle of the product, system, production line, or service from conceptualization till it retires. That’s why it’s becoming a must have for businesses. In fact,
By 2020, 30% of G2000 companies will be using data from digital twins of IoT-connected products and assets to improve product innovation success rates and organizational productivity, achieving gains of up to 25%. – IDC
With this whitepaper, you will learn:
- Digital twin: general concept
- Why digital twin matters
- How to create a digital twin model?
- Implications in different industries
