This award supports a mixed-methods (quantitative and qualitative) approach to identify the barriers and solutions for the adoption of collaborative robots by small- and medium-sized manufacturers (SMMs). The research team will identify barriers to robot adoption by SMMs, analyze labor market and firm data to identify supportive environments for robot adoption, and analyze policy approaches supporting robot adoption among SMMs through two case studies of regions that have a substantially higher level of robot use. The rate of technology and robot adoption among SMMs is much lower than that of large manufacturing firms, and that may be one reason for growing inequality in production. An aging workforce and vacant jobs are also hindering SMMs. If women and minority manufacturing workers who are concentrated in low skill jobs shift to work with collaborative robots, they have the potential for up-skilling without having college level STEM skills and moving into mid-skill/wage jobs. Thus, greater adoption of collaborative robots by SMMs has the potential to increase productivity and competitiveness, upskill jobs and wages, attract women and minority workers, and counter growing inequality. The comparative case studies will provide further insights about the competitiveness of SMMs across the US with respect to regions with significantly higher levels of robot use.
This research project will provide a comprehensive understanding of the barriers to the adoption of collaborative robot adoption. Such barriers disadvantage small and medium size manufacturers (SMMs), their workforce, and local economies. The research team’s mixed-methods approach includes the use of machine learning and topic modeling on novel data such as real time labor market data and microdata from the first census questions on robotics use in manufacturing. This approach contrasts with the usual approach to studying robot adoption, which focuses on measuring the impacts of existing robots on economic out-comes like employment and wages. SMMs can lag in co-robot adoption, not because the technology is inadequate, but because there is insufficient knowledge on how to incorporate it. While a collaborative robot has the capability to aid a company in its manufacturing processes, other prerequisites for full adoption are lacking such as staff with prior robotics knowledge or experience, partners in their supply chain with prior robotics knowledge or experience, and incentives for the robot distributor or supplier to provide more free or low-cost training. The failure to meet these prerequisites acts as barriers to co-robot adoption. This proposal identifies the barriers and strategies for surmounting them, based on insights derived from studying co-robot use in the most robot intensive regions, and, evolving skill demand in small and medium size manufacturers that is correlated with robot use.
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.
