Workshop 2

Workshop 2 is planned and being scheduled as a series of five roundtables, one each on each of the four AI priority areas, followed by a fifth roundtable to summarize R&D recommendations and determine their integration into an industry-wide adoption strategy. Each roundtable will comprise a manufacturing co-chair, an AI co-chair, an organizing committee member lead, a technical writer, and approximately ten AI and manufacturing oriented participants, all with expertise in the particular AI priority area. Each of the four roundtables will conclude with a one hour report out and discussion of findings conducted as an open session for a broader community of recommended and interested participants to hear and contribute. Workshop 2 will produce a cumulative report of Workshops 1 and 2 and will conclude with a seminar on the report when it is ready as a draft expected in September 2021.

The chart above groups the resonant comments from Workshop 1 in a graphic that came together as an implementation framework for driving demand for digitalization and the adoption of AI. When aligned and orchestrated together these four areas of AI priority create an integrated adoption cycle to produce the industry wide network effects in the center that motivate the continuous investment in capability and the adoption of technologies and practice to address U.S. manufacturing industry competitiveness.

The objective of the framework is to depict the key elements needed to secure the critical mass of industry commitment necessary for sustained use of AI and data centric solutions. A cycle of collaboration can start now using proven AI methods to produce tools for today’s workforce and to define workforce training programs that can be updated with industry participation at a pace consistent with technology innovation and industry demand. This is also consistent with a general position taken by the workshop participants that the industry needs to start working with data now with a line of site to what is needed to enable AI in the future.

The blue regions at the top and bottom define the need to use industry-wide and factory specific AI strategies to link supply chains with the factory operations. This linkage is essential to providing benefits that directly impact operations on the factory floor, where the data needed to provide further benefits, is generated. Workshop participants, however, also identified high priority opportunities for AI at the supply chain level that included increasing yield, decreasing waste, preventing single source failure, providing supply chain as a service, shared inventory and capability data, and signals for real time supply and demand changes. Opportunities also included business to business interoperability, open source data for building AI tools, and machine/operations benchmark data. At the factory level, priority AI opportunities include augmenting human involvement, automated product testing and quality assurance, machine/operation monitoring and control, and providing higher quality information to human workers.

The black regions on the left and right address the need to establish industry-wide adoption of collaborative AI infrastructure and workforce strategies. As shown on the left, infrastructure, tools, and practices are needed to enable data sharing with trust. Workshop participants emphasized the need for data that is meaningful, available, accessible, affordable, reusable, sharable, secure, and trusted. The region on the right addresses the need for a workforce that can find and apply AI tools, data and modeling configurations and application knowhow in factory operations, and have the direction and capability to contribute data, information, and knowhow relative to a redefined value proposition for intellectual property.