Korean startups increasingly rely on smart manufacturing
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Smart manufacturing integrates innovative technologies such as augmented reality (AR), the Internet of Things (IoT), blockchain, and 3D printing, coupled with unmanned transportation devices such as autonomous mobile robots, all of which reduce human labor and manufacturing costs.
With the advent of innovative remote and non-face-to-face technologies, the world’s smart manufacturing industry is anticipated to grow at a compound annual growth rate of over 10 percent, hitting $400 billion in 2024 and $455 billion in 2025, according to market research firm TrendForce.
South Korean manufacturing startups are joining the bandwagon.
According to the Korea Federation of Small and Medium Enterprises (SME), manufacturing startups and small companies experienced a significant increase in sales and operating profit after adopting smart factories. Sales for these enterprises increased by 63.4 percent, while operating profit rose by 50.5 percent between 2018 and 2022.
Startups devoted to providing smart manufacturing technology services have also become increasingly popular investments. AI-based analysis data provider Amber Road recently secured seed funding for its solutions, including a data analysis tool applicable to manufacturing sites and a real-time AI operation management solution called Minor Report.
Other AI-based startups are also grabbing investors’ attention and investment for their industrial AI solutions. INEEJI provides customized solutions for heavy industrial factories using AI for tasks such as temperature monitoring in furnaces at steel manufacturing plants.
Industrial AI and big data analysis company INTERX signed an investment agreement with City of Daegu to build an autonomous production demonstration factory and research lab.
MOVIC Lab, a startup that develops AI-generated solutions to machine failure, secured pre-A investment from the Korea Credit Guarantee Fund and equity private firms, including Evergreen Investment Partners, in December 2023. Its solutions involve autonomous equipment failure prediction by collecting and analyzing ultrasonic signals instead of relying on experienced technicians to detect faulty parts.
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