Roundtable 4: AI for Discovery of Capabilities and Solutions

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Once data, information, and application knowhow have been made accessible, they must be made discoverable. In this regard, manufacturing can take inspiration from the world wide web, where information holders voluntarily post information for users, often in the hope of deriving income.  AI has the potential to identify manufacturers who already have the equipment, process plans, and expertise needed to manufacture a needed part by searching for similar parts, materials, machines, or processes manufacturers have previously produced or used. Like case-based reasoning and retrieval, if a library of these parts or materials were accessible, indexable, and searchable, it could serve as the basis for an open marketplace for manufacturing services that would be particularly useful for small- and medium-sized companies that are frequently driven to seek offshore manufacturing sources. A search-based marketplace does not require the customer to possess any process expertise or require the manufacturer to disclose any information to the customer except price and delivery, making it attractive to small- and medium-sized manufacturers with concerns about intellectual property. A similar search function might also allow manufacturers to reuse the data and modeling configurations and setups for commonly used process operations or machines. In general, there are levels of detail in specifying configurations. Several levels of detail could be relatively open without affecting proprietary concerns, but as configuration information becomes more specific and proprietary, sharing would need to become a business transaction. The issue becomes one of recalibrating intellectual property.

The evolution of a networked system for the discovery of manufacturing resources might evolve along similar lines to the evolution of software tools for searching, browsing, and webpage creation on the Internet. Web-based tools evolved explosively to more powerful versions in a few short years in the mid-1990s from Lycos to Google, Mosaic to Internet Explorer, and Front Page to Word under the driving force of accelerating web-based commerce. The potential exists for new software tools to promote a similar expansion of web-based commerce in manufacturing.

  • A manufacturing web could provide the framework for greater interconnectedness and increasing a network effect for application resources.
  • At the operations level, machine learning methods gain power with more data. This provides the potential for creating a data marketplace in which solution providers can purchase and aggregate data from multiple firms and charge them for process control services. But this solution is only viable if the data providers feel confident that their data will be protected.

The points above are about the distribution of AI tools, application capability, and knowhow, but their successful adoption is fundamentally dependent on people. The economics of AI ultimately depend on a close coupling of AI and human centric operations. The roles of people encompass the development of the tools, the development and sustainment of applications, and execution of the solution implementations. Publicly, AI has been associated with job loss, but the reality is that there is significant opportunity in thinking systematically about people, process and technology, especially when scaled across the industry to change the actual work content of manufacturing jobs.