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Advanced Data Exchange Standards for the Biomanufacturing Supply Chain


Supply chain and manufacturing resilience are at the forefront of U.S. economic security, as indicated by relevant House and Senate bills issued in 2020 and 20211. The report on building resilience supply chains and revitalizing American manufacturing released by a Whitehouse task force in 2021 revealed that resilience issues affect several critical manufacturing industry sectors, including aerospace, automotive, semiconductor, biopharmaceutical, and food2. In February 2022, a subcommittee under the Whitehouse’s National Science and Technology Council (NSTC) released the latest update on Critical and Emerging Technologies (CETs) that includes advanced manufacturing and artificial intelligence (AI). These are technologies deemed critical to U.S. economic security that can address the resilience issue according to the NSTC’s report on Strategy for American Leadership in Advanced Manufacturing3. The report indicates the actions needed to help U.S. manufacturers realize the Smart Manufacturing CET and the Infrastructure for AI CET that include: 1) facilitate a digital transformation in the manufacturing sector; 2) develop standards that enable seamless integration between smart manufacturing components; and 3) develop best practices, new approaches, and standards to provide consistent and reliable access to manufacturing data within and across industries (i.e., within and across supply chains).

Manufacturing enterprises, including agri-food, biomanufacturing, and others, are embarking on their digital transformation journeys by embracing the emerging data economy and artificial intelligence (AI). In doing so, they need their supply chain, business, and engineering workflows to be easily adapted and data from the supply chain to internal operations to be easily connected and understood. For example, raw material quality data from suppliers need to be available in advance for material requirement planning, recipe adjustment, and adaptive control in biopharmaceuticals. Such digital transformation will expose entire silos of application and process data, whose sources can number in hundreds or thousands. While technologies and methods for exposing, extracting, and integrating data through standards have been available; the sheer size, the increase in accessibility requirements, and the dynamic nature of applications and process data imposed by the manufacturing agility goal necessitate further advancements to these technologies and methods. The current approaches to integrations and data access provisions are still too expensive, too rigid, and too time-consuming to develop, change, and maintain, thereby stifling agility and resilience. The advancements are needed not only on the approaches but also on the coverage, semantic precision, and accessibility of the standard.





The objective of this project is to develop measurement science that includes methods, workflow models, information meta-models, interoperability metrics, and prototype tools to help U.S. manufacturers reduce the time and cost associated with making supply chain data accessible for better supply chain planning and resilience.

Technical Idea
The technical goal is to develop methods and tools that allow supply chain partners to connect/reconnect and make data available digitally faster, cheaper, and with higher quality. The current focus is on improving the efficiency around the standard heterogeneity through better data mapping management. There are four prongs to the technical idea – a) investigate artificial intelligence methods to assist humans in the mapping task; b) decompose the mapping task (into at least domain expert task and programmer task; c) develop new methods, mapping languages and tools to support those subtasks; and d) investigate distributed or federated-learning standard development and use architecture to support mapping reuse.

Research Areas
The project will address the three actions relevant to the U.S. industrial competitiveness and economic security as identified in the NSTC’s report on Strategy for American Leadership in Advanced Manufacturing4. These are 1) facilitating a digital transformation in the manufacturing sector, 2) developing standards that enable seamless integration between smart manufacturing components, and 3) developing best practices, new approaches, and standards to provide consistent and reliable access to manufacturing data within and across industries. The technologies needed for these objectives are critical to the U.S. and are identified as Critical and Emerging Technologies (CETs) in the most recent NSTC report released in February 2022. To that end, our research areas include:

  • Reengineering the standard lifecycle management that includes development, use, and mapping approaches;
  • Developing a new meta-model, standard, and guideline for syntax-independent standard lifecycle management to improve the technology scalability of integration assets;
  • Applying AI to facilitate more efficient standard lifecycle management tasks; 
  • Developing new metrics to measure improvements; and
  • Working with industry across overlapping supply chains to ensure that our solutions truly can address today’s complex, cross-industry challenges.



Created April 15, 2024, Updated April 26, 2024