October 30, 2024 By Cullen SmithProject Manager, Research The ongoing smart manufacturing transition represents a pivotal shift in the manufacturing sector, creating a highly interconnected, automated, and data-driven environment that promises enhanced efficiency, productivity, and innovation.1 However, traditional classification systems like the North American Industry Classification System (NAICS) and Standard Occupational Classification (SOC) systems, while crucial for categorizing manufacturing fields, face limitations when applied to rapidly evolving sectors like advanced manufacturing. These limitations have far-reaching implications for preparing a workforce capable of succeeding in future industries while maintaining current operational standards and underscore the importance of adapting to ensure success in manufacturing imperatives. The swift development of new manufacturing technologies often outpaces updates to NAICS and SOC codes. As traditional practices merge with emerging technologies like automation, robotics, and artificial intelligence, existing classification systems struggle to accurately reflect these hybrid sectors. For example, the O*NET (Occupational Information Network) database provides detailed occupational information but faces challenges in addressing the needs of advanced manufacturing sectors that overlap with traditional industries. Additionally, concerns about the timeliness of updates in government databases hinder accurate trend reflection in rapidly evolving fields.2 To tackle these challenges, the research team at SME is leveraging several methods to try and adjust our analysis and “crack the code” surrounding evolving industries and classification standards. Some of those methods include: Innovative Tracking: The SME research team can utilize public and proprietary databases and tools to enable concurrent identification and measurement of emerging manufacturing sectors. Analyzing keywords, patents, business registrations, and job postings allows us to predict and synthesize trends around innovative new industries.3 The addition of regional and custom-based research approaches offers some insight. Still, we must be careful about how we utilize these methods.4 Aligning Education with Labor Market Needs: The SOC and Classification of Instructional Programs (CIP) codes link the economy to educational offerings. Continuous assessment of educational program alignment with labor market demands is critical as new manufacturing fields emerge. Combining qualitative and quantitative methods, such as surveys and interviews with industry leaders and educators, can help identify specific skill sets needed in the evolving landscape.5 Impact of Automation on Workforce Dynamics: Technological advancements necessitate new skills among manufacturing workers. Industries facing high automation risks must prioritize reskilling efforts to prepare the workforce for future demands. Identifying these skills is essential for developing effective training programs aligned with future needs.6 Closing the Gaps with New Methods: Regular assessments are crucial to ensure educational programs meet the evolving manufacturing landscape's needs. Utilizing both qualitative and quantitative research methods can provide insights into the specific skills required in new sectors, helping bridge gaps in workforce training and education. 7 The future of manufacturing—and the growth of the skilled workforce essential for its success—hinges on our ability to adapt and innovate. As NAICS and SOC codes struggle to keep pace with the sector's rapid evolution, embracing new methodologies is crucial for accurately identifying and tracking opportunities in emerging fields. By adopting these approaches, we can better prepare both businesses and workers to thrive in the industries of tomorrow. References 1 https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-are-industry-4-0-the-fourth-industrial-revolution-and-4ir2 Kirchain3 Kauffman, Robert. “Big Data in Real-Time: The Future of Industry Tracking.” Journal of Data Science, vol. 18, no. 2, 2020, pp. 145-162.4 Upjohn5 CIP TO SOC Crosswalk6 https://systemoffice.kctcs.edu/institutional-research/research-briefs-studies/a-look-at-automation-risk-in-occupations-and-kctcs-programs.aspx7 National Center for Education Statistics Bessen, James E. “How Computer Automation Affects Occupations: Technology, Jobs, and Skills.” Boston University School of Law, 2019. Cohen, E., et al. “Emerging Technologies and Industry Classification.” Research Policy, vol. 50, no. 5, 2021, 104270. Combemale, Bernard, et al. “Workforce Changes in Advanced Manufacturing: Evidence from the Integrated Photonics Industry.” Advanced Manufacturing, vol. 2, no. 1, 2021. National Center for Education Statistics. Classification of Instructional Programs (CIP). U.S. Department of Education, 2022. World Economic Forum. “The Future of Jobs Report 2020.” 2020.
