“AI-powered digital twins mark a major evolution in the future of manufacturing, allowing real-time visualization of the entire production line, not just individual machines,” said Indranil Sircar, global CTO for the manufacturing and mobility industry at Microsoft. “This allows manufacturers to go beyond isolated monitoring and gain much broader information.”
For example, a bottling line digital twin can integrate 1D shop floor telemetry, 2D plant data, and 3D immersive modeling into a single operational view of the entire production line to improve efficiency and reduce costly downtime. John Sobel, co-founder and CEO of Sight Machine, an industrial artificial intelligence company that partners with Microsoft and NVIDIA to transform complex data into actionable insights, estimates that many high-velocity industries have downtime rates as high as 40%. By tracking micro-stoppages and quality metrics through digital twins, companies can target improvements and adjustments with greater precision, saving millions of dollars in once-lost productivity without disrupting ongoing operations.
AI offers the following opportunity. Sircar estimates that up to 50% of manufacturers are now using AI in production. This is more than 35% of manufacturers surveyed in MIT Technology Review 2024 Report who said they have begun introducing AI use cases into production. According to the report, large manufacturers with more than $10 billion in revenue are significantly ahead of the competition, with 77% of them already implementing AI use cases.
“Manufacturing has a lot of data and is an ideal use case for AI,” says Sobel. “An industry seen by some as a laggard in digital technology and artificial intelligence may be in a better position to lead the way. This is very unexpected.”
This content was created by Insights, the content creation arm of MIT Technology Review. It was not written by the editors of MIT Technology Review. It has been researched, developed and written by writers, editors, analysts and illustrators. This includes writing surveys and collecting survey data. The AI tools that could be used were limited to secondary manufacturing processes that had undergone extensive human testing.
