基于PMC指数模型的我国智能制造产业政策文本量化研究
A Quantitative Study of China’s Intelligent Manufacturing Industry Policy Text Based on the PMC Index Model
摘要: 随着全球制造业向智能化、数字化转型加速,智能制造产业已成为提升国家制造业竞争力、推动经济高质量发展的关键领域。本文通过构建智能制造产业政策评价标准体系,运用PMC指数模型对我国智能制造产业政策进行量化分析,将结果通过PMC评分表与PMC曲面图客观地呈现,能够为智能制造产业政策的发展提供更加全面的政策建议与指导。结果显示:选取的13项智能制造产业政策中,8项政策评价良好,5项政策评价可接受。我国智能制造产业政策在政策时效、政策保障措施、政策发布机构等方面存在不足,如政策时效较短、政策保障措施在公共服务能力、营商环境优化和国际合作方面较为薄弱,政策发布机构较为单一。基于此,提出延长政策时效、完善产业支撑、丰富政策发布机构等建议,以推动智能制造产业高质量发展。
Abstract: With the accelerated transformation of the global manufacturing industry towards intelligence and digitalization, the intelligent manufacturing industry has become a key area for enhancing the competitiveness of a nation’s manufacturing sector and driving high-quality economic development. This study constructs an evaluation criteria system for intelligent manufacturing industry policies and employs the PMC index model to conduct a quantitative analysis of China’s intelligent manufacturing industry policies. The results are objectively presented through PMC score tables and PMC surface maps, providing more comprehensive policy recommendations and guidance for the development of intelligent manufacturing industry policies. The results show that among the thirteen selected intelligent manufacturing industry policies, eight policies are rated as good and five policies are rated as acceptable. China’s intelligent manufacturing industry policies have shortcomings in policy duration, policy support measures, and policy issuing institutions, such as short policy duration, weak policy support measures in public service capacity, business environment optimization, and international cooperation, and a relatively single policy issuing institution. Based on this, recommendations are proposed to extend policy duration, optimize the policy support system, and diversify policy-issuing agencies, so as to promote the high-quality development of the intelligent manufacturing industry.
文章引用:赵娜娜. 基于PMC指数模型的我国智能制造产业政策文本量化研究[J]. 运筹与模糊学, 2025, 15(3): 414-421. https://doi.org/10.12677/orf.2025.153172

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