As information technology has matured, a Fourth Industrial Revolution?often referred to as Industry 4.0?is emerging. Industry 4.0 aims to use technology to integrate design, manufacturing, and consumer activities seamlessly, resulting in increased productivity, reliability and customer satisfaction. However, the process of transitioning to Industry 4.0 can be challenging to both companies and workers. Companies cannot stop operating while their production systems and processes are being updated. Due to the cost of the technology and the need to train the workforce, the transition may need to take place over a period of years. Workers who cannot adapt to the new technologies and processes risk losing their jobs. This project will bring manufacturing industry representatives and researchers together to develop a plan for future research. The research plan will lay the groundwork for development of tools and technologies to make the transition to Industry 4.0 more straightforward and cost-effective for companies and more successful for workers. Examples of possible research topics include road maps for transitioning from conventional manufacturing systems to Industry 4.0 systems; tools for ergonomic design of workstations and assembly lines; cognitive virtual assistants for workers; guidance for human interface design; and automated generation of knowledge and skill ?crosswalks? to help companies identify existing skills that will still be needed and new skills that workers need to learn.
The proposed planning grant aims to develop a research agenda related to the implementation of Industry 4.0 within the manufacturing industry. The work domain is manufacturing; sub-domains could include smart design, smart machining, smart assembly, smart monitoring, smart control and smart scheduling. The workplace is the factory, which could include production lines, engineering design, and inspection and testing areas. The workers include the engineers and operators who work in these areas. The proposed planning activities will focus on gaining a better understanding of industry needs which, together with a convergent research approach, will lay the groundwork for development of tools and technologies that can enhance the human-technology partnership and augment human performance in Industry 4.0 manufacturing work. Major tasks will include: (1) survey Industry 4.0 manufacturing system development, applications, and constraints; and (2) develop a convergent research agenda in close collaboration with representatives from a variety of manufacturing sectors and researchers with expertise in manufacturing engineering, human factors, human resource development, sociology, and computer science. Intellectual merit: Although Industry 4.0 has many potential benefits, at this time, the process of transitioning from a traditional manufacturing system to an Industrial 4.0 manufacturing system is expensive and fraught with risk to both the company and its workers. The proposed convergent approach will allow scientists to learn from plant technical managers about the operational challenges of implementing Industry 4.0 within their companies. Involving researchers with diverse areas of expertise will provide a richer understanding of the issues and convergent approaches to problem-solving. Broader impacts: Industry 4.0 is projected to add $2.2 trillion to domestic GDP by 2025. The value of the operational transformation to the global manufacturing industry is estimated to be $3.7T/year. To remain competitive, manufacturers need to be able to rapidly adapt to changing markets, which requires having a well-prepared workforce. The proposed planning activities will identify tools and technologies that could be developed to enable successful implementation of Industry 4.0, and thereby enhance work environments, position workers to be successful at their jobs, and increase competitiveness of U.S. companies.
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.
