EAGER: Integrating Small and Medium Sized Manufacturing Enterprises (SMEs) into the Next Generation Manufacturing Ecosystems for National Security and Self Sufficiency

This EArly-concept Grant for Exploratory Research (EAGER) project focuses on establishing rigorous foundational artificial intelligence and network science-based strategies to represent data, build search strategies, and help form clusters of Small and Medium Manufacturing Enterprises (SMEs) to meet production demands. SMEs form the backbone of our country’s economic prosperity, community welfare, and self-sufficiency. Strengthening the USA’s manufacturing base will help in the nation to become less dependent on global supply chains, thus helping national security and self-sufficiency. However, finding and selecting relevant SMEs for a specific product currently requires manual consultation of databases, manual collation of data, and compiling information which is not ideal in the age of information explosion. This research will help establish the scientific foundation for representing SMEs data, provide for distilling the relevant information about the SMEs of user’s interest, and facilitate building collaborations among SMEs. By providing such methodologies, this effort will help enhance an indigenous manufacturing base. This award will also result in enhanced national security by providing efficient provenances for all manufactured products. Many SMEs in the USA tend to have 3rd or 4th generation workers and are predominantly rurally located. The efforts from this award will help in establishing a platform for the manufacturing ecosystems for the future wherein diverse SMEs irrespective of location and scale, will have an equal opportunity to participate.The research focus is on using a network science-based representation to connect SMEs as graph database formalisms that offer representation power, help build efficient search mechanisms, and offer intuitive methods to connect SMEs to fulfill the demand that cannot be met by a single provider. Machine learning and economic theory based foundational algorithms resulting from this research will help in creating optimal search and coalition formation among SMEs. The scientific foundations of this research will be developed through SME inspired use cases, thus addressing both theoretical and engineering considerations to make this research generalizable to all SMEs. By the nature of design, the methodologies will help build a high level of data security and long-term sustainability of the ensuing platform. This award will address: 1) data models for representing SMEs, 2) search mechanisms to find SMEs based on specific contexts, 3) composition of SMEs to meet the requirements of larger enterprises in an economic and sustainable manner, and 4) federated learning schemes to recommend relevant SMEs for specific contexts. Graph databases offer a unique representation formalism that is dynamic, flexible, and scalable, making them suitable for addressing these research problems. Additionally, game theoretic and auction-based mechanisms will be used to compose SMEs and help in supplier selection. Real-time issues, dynamic updates, and scaling up of computational considerations are considered in this research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.