Digital twin in manufacturing: a must-have in Industry 4.0
by
Andrey Koptelov
for
iTransition
Recommended by
The author touches on why digital twins are only gaining traction now, before defining the role of a digital twin in a modern production plant.
Finally, the author briefly outlines key use cases, including a ranking of business imperatives gathered in 2021 with Covid-19-driven desire for digital transformation.
Why it’s relevant to Nextspace
This article is relevant to Nextspace Partners as it approaches the role of digital twins in manufacturing from the customer's perspective.
- Instant access to comprehensive information about an asset or system without having to involve other people.
- The ability to convert a wide range of complex data into evidence-based adjustments.
- Model processes with operational data to identify inefficiencies.
- Monitor and benchmark asset and operational condition to highlight potential failures for predictive maintenance (non-quantified examples given being Kaeser and Stara).
- Monitor operation across a product’s lifecycle to gain insight for performance upgrades.
- Test innovation virtually where that is cheaper than physical trials (non-quantified examples given being Bridgestone and Michelin).
- Measure product quality during production, in time to improve it.
The article is short on quantified results but does mention a Maserati digital twin case study where a digital twin avoided the need to place physically complete cars into a wind tunnel for testing. This and similar benefits decreased operational cost and time by 30%, and time to market by 16%.
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