NRI: FND: Barriers and Solutions for Small and Medium Sized Manufacturers Collaborative Robot Adoption

该奖项支持混合方法（定量和定性）方法，以确定中小型制造商（SMMs）采用协作机器人的障碍和解决方案。研究小组将确定中小企业采用机器人的障碍，分析劳动力市场和企业数据，以确定支持机器人采用的环境，并通过对机器人使用水平高得多的地区的两个案例研究，分析支持中小企业采用机器人的政策方法。中小企业对技术和机器人的采用率远低于大型制造企业，这可能是生产不平等日益加剧的原因之一。劳动力老龄化和职位空缺也阻碍了中小企业的发展。如果专注于低技能工作的女性和少数族裔制造业工人转向与协作机器人一起工作，他们就有可能在没有大学水平STEM技能的情况下提高技能，并进入中等技能/工资工作。因此，中小企业更多地采用协作机器人有可能提高生产率和竞争力，提高技能工作和工资，吸引女性和少数民族工人，并应对日益严重的不平等。比较案例研究将提供关于美国中小企业相对于机器人使用水平明显较高的地区的竞争力的进一步见解。本研究项目将提供对协作机器人采用障碍的全面理解。这些壁垒不利于中小型制造商、他们的劳动力和当地经济。该研究团队的混合方法包括使用机器学习和对新数据（如实时劳动力市场数据和来自机器人在制造业中使用的第一次人口普查问题的微数据）进行主题建模。这种方法与研究机器人采用的通常方法形成对比，后者侧重于衡量现有机器人对就业和工资等经济结果的影响。中小企业在采用协作机器人方面可能会落后，不是因为技术不足，而是因为对如何整合它的知识不足。虽然协作机器人有能力在制造过程中帮助公司，但全面采用的其他先决条件缺乏，例如具有先前机器人知识或经验的员工，供应链中具有先前机器人知识或经验的合作伙伴，以及机器人分销商或供应商提供更多免费或低成本培训的激励措施。如果不能满足这些先决条件，就会成为采用协作机器人的障碍。基于对机器人密集地区协同机器人使用的研究，以及与机器人使用相关的中小型制造商不断发展的技能需求，本提案确定了克服这些障碍的障碍和策略。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.