Options for a National Plan for Smart Manufacturing (2024)
Chapter: Executive Summary
Executive Summary
This report is the result of a study by an ad hoc committee appointed by the National Academies of Sciences, Engineering, and Medicine tasked with developing options for a national plan for smart manufacturing technology development and deployment. It examines technical frameworks and processes; identifies possible timelines and necessary resources to achieve continued success in smart manufacturing; and explores policies and general roles for government, industry, and academia to address near-, medium-, and long-term challenges to improve the productivity and energy efficiency of the manufacturing sector of the United States and ensure U.S. competitiveness. A particular focus is given to system integration issues, including incorporating manufacturing science, materials science, energy science, and other critical domains.
The Committee on Options for a National Plan for Smart Manufacturing held five web-based information-gathering sessions as well as three hybrid (online and in-person) workshops. The workshops were held at National Academies facilities in Washington, DC, and covered the following topical areas:
- State-of-the-art smart manufacturing and future directions and needs (February 6, 2023);
- Education, training, and workforce needs for smart manufacturing (February 28, 2023); and
- Potential broader impacts of smart manufacturing (March 7–8, 2023).
Smart manufacturing fundamentally is about what gains are possible when the required data are available in facilitative formats, when and where needed throughout the enterprise, to apply the best machine or human actions to make the best product with the fewest resources. Importantly, smart manufacturing is also about using these data to control, manage, and optimize at the scale of enterprises ranging from factory operations to supply chains and industry ecosystems. Many of the elements of smart manufacturing have been in existence for decades; however, with the advent of cost-effective and ubiquitous technologies such as high-speed communication, cloud and distributed computing and sensing, and advanced human–machine interaction, smart manufacturing has the tools to scale. Smart manufacturing’s greater opportunity lies with its scale, comprehensiveness, and interconnectedness, which can optimize raw material-to-product use; improve the productivity, precision, and performance of factories and supply chains; and address the increasing complexities of economic, social, and environmental sustainability that need to be managed and optimized in today’s competitive landscape. Significant advancement and opportunities lie at the convergence of information technology (IT) and operational technology (OT) as well as in the upskilling of the workforce to make them more valuable, secure, and productive. This will, in turn, provide competitive-wage jobs as well as secure a variety of job functions while ensuring the stability of the U.S. manufacturing base for the long term. Thus, continuous education and workforce development are paramount to the success of smart manufacturing and ultimately the U.S. manufacturing ecosystem. These educational issues combined with technical advances and the ability to leverage these advances throughout the entire manufacturing ecosystem, from large multinational corporations to small and medium-sized enterprises, are essential for smart manufacturing to flourish and provide the maximum impact.
The report is divided into four chapters and a concluding analysis. Chapter 1 describes the value and state of smart manufacturing in the United States, provides key definitions for a more in-depth discussion of smart manufacturing, and discusses the importance of data and scale. Chapter 2 addresses challenges in current and next-generation workforce development and training. Community colleges and 4-year colleges are currently the main vehicles for providing workers with the technical credentials needed for entry-level jobs in smart manufacturing. To be most effective, instructors must stay abreast of new developments in smart manufacturing, and new educational models such as “learn and earn” and state-sponsored apprenticeship programs must be developed to ensure that the current and next-generation workforce can remain relevant and that training opportunities remain accessible to all. While many organizations are engaged in developing curricula and training workers for careers in smart manufacturing, none are primarily focused on these tasks. Current efforts are fragmented, leading to significant gaps
and inefficient duplication of work. Manufacturers, as a result, see the lack of a skilled workforce as the leading bottleneck for their operations.
To address these workforce challenges, the committee recommends the development of a holistic national approach to coalesce and more comprehensively align resources to handle the volume of immediate need but also the pace of technology changes that will need to be accommodated for the future. A comprehensive approach leverages, supports, and amplifies existing workforce development infrastructures, investments, and systems. Education-focused initiatives that provide consistent, scalable, efficient, and industry-driven resources and that are more boldly chartered and orchestrated are needed to ensure that the current and future manufacturing workforce in the United States is relevant, innovative, and adaptable to the changing technology and economic landscape of the manufacturing industry. Several additional steps are envisioned to address workforce training, and education challenges to increase the number of workers with credentials in smart manufacturing. A workforce that is well trained in smart manufacturing technologies is vital to the nation’s economy and defense, the reduction of U.S. industry’s energy footprint, and numerous other issues for which specific U.S. departments or agencies are responsible.
Chapter 3 discusses the next generation of smart manufacturing technologies. In particular, it discusses that the creation, validation, and curation of data are of utmost importance to smart manufacturing. The ability to readily and securely access, utilize, and update manufacturing data throughout the industry is a fundamental necessity for smart manufacturing. Therefore, the committee recommends that the Department of Energy in partnership with the National Institute of Standards and Technology, the Department of Defense, and manufacturing institutes establish mechanisms and methods for validated, standardized manufacturing data banks to enable cybersecure collaboration and facilitate various levels of autonomy within manufacturing and supply chain operations.
Chapter 4 provides potential directions forward related to the roles in industry, academia, and the government for smart manufacturing in the United States. This chapter also provides some insight into the resources that will be needed to increase the adoption of smart manufacturing throughout the entire U.S. manufacturing ecosystem (e.g., resources for small and medium-sized enterprises to update and secure their current operations by deploying smart manufacturing technologies). This includes new equipment and infrastructure to support increased secure data-sharing and curation needs.
Finally, Chapter 5 concludes the report with an additional discussion on implementing the recommendations and a summary of the committee’s key recommendations.
SUMMARY LIST OF KEY RECOMMENDATIONS FROM THE REPORT
Key Recommendation: A national plan for smart manufacturing should offer a holistic, boldly orchestrated national approach to solve workforce challenges and leverage, support, and amplify existing workforce development infrastructures, investments, and systems. An effective initiative could take the form of an independent nongovernmental institute or organization, such as a National Academy for Smart Manufacturing Education and Training, that is chartered to drive workforce-related initiatives and support smart manufacturing education and training in the United States.
Key Recommendation: A national plan for smart manufacturing should urgently support the establishment of national transformative data infrastructure, tools, and mechanisms to assist with (1) cultivating, selectively sharing, and securing the use of data in real time and at scale; and (2) sharing best practices to promote industry-wide technical data standards. Such infrastructure, developed as a national capability, could take the form of a secure digital network that facilitates the flow of data with controlled and credentialed access, such as a Cyber Interstate. It should be planned and coordinated with companies, government agencies, associations and consortia, and academic stakeholders.
Key Recommendation: The Department of Energy in partnership with the National Institute of Standards and Technology, the Department of Defense, and manufacturing institutes should establish manufacturing CASE (Calibration, Autonomy, Security, Evaluation) Data Banks with the next generation of secure manufacturing architectures.
Key Recommendation: Smart manufacturing is multifaceted, and technologies developed in one specialty area most likely will not be suited for other applications. The Department of Energy and other federal agencies should fund programs and consortia that develop technologies at the intersections of critical technologies (e.g., human–artificial intelligence [AI] co-piloting, sensing, AI/machine learning, platform technologies, digital twins, uncertainty quantification), unit manufacturing processes (e.g., casting, forming, molding, subtractive, additive, and joining), and industry sectors (e.g., semiconductor, aerospace, automotive, biomedical, and agriculture).
Key Recommendation: Funded by the Department of Energy, in consultation with other relevant federal departments and agencies, a framework should be developed to quantify the broader sustainability benefits
of implementing secure smart manufacturing (considering three pillars: environment, economy, and society) as well as industry-wide sustainability metrics.
