United States Government Publishing Office publisher pbl distributor dst United States Commerce Department National Institute of Standards and Technology (NIST) author aut Government Organization St Pierre, James A. author aut text government publication eng GOVPUB Executive Agency Publications executive 2023-10-27 Commerce Department 2022-09-14 monographic deposited born digital 95 digital object pages C 13. https://www.govinfo.gov/app/details/GOVPUB-C13-a547c3b6896d64d191477347ad797587 P0b002ee1c1ba3500 on1389890459 (OCoLC)1389890459 DGPO 2023-10-27 2026-06-27 GOVPUB-C13-a547c3b6896d64d191477347ad797587 machine generated eng fdlp C13 GOVPUB-C13-a547c3b6896d64d191477347ad797587 a547c3b6896d64d191477347ad797587 GOVPUB NIST Advanced Manufacturing Series NOTE: THE “DATE ISSUED” ABOVE MAY DEFAULT TO JANUARY 1ST OF A GIVEN YEAR. TO THE VIEW THE MOST ACCURATE DATE OF ISSUE, REVIEW THE TITLE PAGE OF THE PUBLICATION. The Advanced Manufacturing Series consists of reports, guides, recommendations, specifications, use cases, and data management methods related to the design, planning, implementation and operation of advanced manufacturing. Topics cover, but are not limited to, additive manufacturing, advanced composites, digital manufacturing, flexible hybrid electronics, integrated photonics, lightweight metals, and smart manufacturing. AMS is divided into subseries for specific groups and document types. National Institute of Standards and Technology Commerce Department. National Institute of Standards and Technology. 2022 2022-09-14 https://www.govinfo.gov/app/details/GOVPUB-C13-a547c3b6896d64d191477347ad797587 https://www.govinfo.gov/content/pkg/GOVPUB-C13-a547c3b6896d64d191477347ad797587/pdf/GOVPUB-C13-a547c3b6896d64d191477347ad797587.pdf Symposium Report Advanced manufacturing Artificial intelligence Business logistics Digital manufacturing Intelligent manufacturing Machine learning Manufacturing processes Smart manufacturing Supply chain Workforce development Workforce education St Pierre, James A. creator St. Pierre, James A. National Institute of Standards and Technology (U.S.) Engineering Laboratory mdu U.S. Dept. of Commerce, National Institute of Standards and Technology 2022-09-14. monographic 1 online resource (95 pages) : illustrations (color) text technical report eng In 2020, the National Science and Technology Council (NSTC) Subcommittee on Advanced Manufacturing and Subcommittee on Machine Learning and Artificial Intelligence articulated cross-agency interest in the value and timeliness of organizing a symposium to outline benefits of AI adoption in manufacturing and identify issues that inhibit widescale adoption. In response, the National Science Foundation (NSF) and the National Institute of Standards and Technology (NIST) sponsored a three-workshop Symposium entitled Strategy for Resilient Manufacturing Ecosystems Through Artificial Intelligence (AI) to seek input from industry, academia, and government experts. The workshops focused on potential roles of each sector in performing research and development (R&D) and ideas for implementation of AI in manufacturing that could inform the development of the U.S. Advanced Manufacturing strategic plan. This paper documents the Symposium recommendations with a roadmap organized to overcome the current lack of industry tools, trust, confidence, and experience with industry R&D focused on the barriers to AI scaling and deployment. There are eight specific recommendations for collaborative actions by industry, government, and academia: 1. Establish new and/or expanded Public Private Partnerships (PPPs) to facilitate a broad range of R&D. 2. Research, develop, and demonstrate advanced software tools, models, and infrastructure for AI/ML implementation and scale-up in manufacturing. 3. Establish programs that achieve industry collaboration on an integrated and trusted set of shared capabilities. 4. Initiate R&D to enable industry-wide scaling and deployment of AI applications in manufacturing. 5. Educate and train a digital-savvy manufacturing workforce with software and hardware tools needed to deploy and scale the use of data and AI with trust and confidence. 6. Enable digital capabilities at small and medium-sized manufacturers (SMMs). 7. Incentivize AI adoption throughout established supply chains. 8. Promote new business models for AI adoption. As this adoption cycle takes hold, the market-driven forces of entrepreneurship and investment capital will ultimately lead to industry-wide adoption of AI technology, and the U.S. manufacturing industry will be on its way to achieving global leadership and resilient supply chains. James A. St Pierre. September 2022. Title from PDF title page (viewed January 4, 2023). Includes bibliographical references. Approved by the NIST Editorial Review Board on 2022-04-05 Mode of access: World Wide Web. Systems requirements: Adobe Acrobat PDF reader. Artificial intelligence Machine learning Manufacturing processes Business logistics Advanced manufacturing Digital manufacturing Workforce education Workforce development Intelligent manufacturing Smart manufacturing Supply chain https://doi.org/10.6028/NIST.AMS.100-47 symposium report symposium report