NIST to launch competition for AI-focused Manufacturing USA institute

The U.S. Department of Commerce’s National Institute of Standards and Technology (NIST) plans to announce an open competition for a new Manufacturing USA institute focused on using AI to improve the resilience of U.S. manufacturing. NIST anticipates that $70 million in federal funds will be invested in this new institute over five years, with an equal or greater contribution from private and other nonfederal funding sources.

NIST has published a Notice of Intent (NOI) in the Federal Register to allow potential applicants sufficient time to develop meaningful collaborations among industry, academia, federal laboratories, and state and local government agencies.

“Manufacturers that make smart use of AI to improve operational performance and strengthen supply chains will be more productive and resilient as they compete in an increasingly crowded global marketplace,” said Under Secretary of Commerce for Standards and Technology and NIST Director Laurie E. Locascio. “We look forward to reviewing applications for a new Manufacturing USA institute that will strengthen the national economy by helping domestic manufacturers maximize the potential of AI.”

Manufacturing USA is a national network of institutes that brings together people, ideas, and technology to solve advanced manufacturing challenges. All Manufacturing USA institutes are public-private collaborations focused on technology, supply chain, and education and workforce development.

The AI institute will be a new addition to this network and will focus on conducting R&D projects, establishing employer-led sectoral partnerships to develop training resources, and creating pathways for the skilled workforce needed to move innovation into industrial practice. NIST expects the AI institute to achieve time-bound outcomes that support manufacturing resilience such as accelerating adoption of new technologies, performing predictive maintenance, optimizing manufacturing processes and working capital, and predicting and mitigating risks from supply chain disruptions.