Towards Resilient Manufacturing Ecosystems Through Artificial Intelligence: Symposium Report

Summary

Publication Title: Towards Resilient Manufacturing Ecosystems Through Artificial Intelligence: Symposium Report

Series Title: NIST Advanced Manufacturing Series

Personal Author: St Pierre, James A.

Publication Number: NIST AMS; 100-47

Date Issued: 2022

Subject: Artificial intelligence
Machine learning
Manufacturing processes
Business logistics
Advanced manufacturing
Digital manufacturing
Workforce education
Workforce development
Intelligent manufacturing
Smart manufacturing
Supply chain

Government Author: Commerce Department, National Institute of Standards and Technology (NIST) .

Category: Executive Agency Publications

SuDoc Class Number: C 13.

Abstract: 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.