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

NRI：FND：中小型制造商采用协作机器人的障碍和解决方案

基本信息

批准号：
2024706
负责人：
Nancey Green Leigh
金额：
$ 75万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024706&HistoricalAwards=false
关键词：
NRI FND Barriers Solutions Small

项目摘要

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.

该奖项支持混合方法（定量和定性），以确定中小型制造商（SMM）采用协作机器人的障碍和解决方案。研究小组将确定SMM采用机器人的障碍，分析劳动力市场和公司数据，以确定机器人采用的支持环境，并通过两个机器人使用水平高得多的地区的案例研究，分析支持SMM采用机器人的政策方法。SMM的技术和机器人采用率远低于大型制造企业，这可能是生产不平等加剧的原因之一。老龄化的劳动力和空缺的工作岗位也阻碍了SMM。如果集中在低技能工作的妇女和少数民族制造业工人转向与协作机器人一起工作，他们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，SMM更多地采用协作机器人有可能提高生产力和竞争力，提高工作和工资，吸引妇女和少数民族工人，并对抗日益严重的不平等。比较案例研究将提供关于SMM的竞争力在美国相对于具有显着更高的机器人使用水平的地区的进一步见解。这个研究项目将提供一个全面的理解的障碍，采用协作机器人采用。这些壁垒不利于中小型制造商、其劳动力和当地经济。该研究团队的混合方法包括使用机器学习和主题建模新数据，如真实的时间劳动力市场数据和微观数据从第一次人口普查问题的机器人在制造业中的使用。这种方法与研究机器人采用的通常方法形成鲜明对比，后者侧重于衡量现有机器人对就业和工资等经济成果的影响。SMM在协作机器人采用方面可能会滞后，不是因为技术不足，而是因为缺乏关于如何整合它的知识。虽然协作机器人有能力在制造过程中帮助公司，但缺乏全面采用的其他先决条件，例如具有先前机器人知识或经验的员工，具有先前机器人知识或经验的供应链合作伙伴，并激励机器人分销商或供应商提供更多免费或低成本的培训。未能满足这些先决条件是采用协作机器人的障碍。该提案基于对机器人密集地区协作机器人使用的研究，以及与机器人使用相关的中小制造商不断变化的技能需求，确定了障碍和克服这些障碍的策略。该奖项反映了NSF的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。