Stimulating effects of intelligent policy on the performance of listed manufacturing companies in China

Liu, Jun; Y, Yang; Cao, Yaru; Forrest, Jeffrey Yi‐Lin

doi:10.1016/j.jpolmod.2021.01.005

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Based on this, this article adds new indicators on the basis of the previous studies. For example, using policy texts to extract low-carbon indicators [37] and intelligent manufacturing indicators [40], calculating the number of scientific research cooperation networks in different cities based on web of science [49], and measuring the carbon emissions of each city [36], thus enriching the indicator system of UAM.…”

Section: Discussionmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

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With the rapid development of the economy, it is important to reasonably evaluate the development status of the regional manufacturing industry. Given this, this article expands the evaluation indicators of urban advanced manufacturing (UAM) from the perspective of the push–pull-mooring (PPM). Then, it uses a machine learning (ML) method to predict the evaluation results of other cities through a small amount of sample data. The results show that: (1) From the current development status of UAM in Guangdong Province (GD), cities in the Pearl River Delta region occupy a dominant position. However, cities in eastern, western, and mountainous regions have strong development potential and lead cities. Therefore, each region has cities with high levels of development and has a demonstrative role. (2) By comparison, it was found that the overall development level of UAM in GD is not significantly different from that of the Yangtze River Economic Belt. However, due to significant differences in their extreme values, the proportion of cities above the average in the overall population is relatively small. This indirectly proves that GD’s UAM not only has a phased nature, but also has a demonstrative role. (3) The prediction effect of the perceptron model is better than other methods. Although neural network models have better prediction performance than other machine learning models, they should not overly rely on complex network structure prediction data. By comparing the results, the reliability is verified. Finally, according to the life cycle theory, we propose a targeted development path for different UAM.

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Section: Discussionmentioning

confidence: 99%

The Evaluation Prediction System for Urban Advanced Manufacturing Development

Dou,

Sun,

Zhu

et al. 2023

Systems

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“…In order to build intelligent process automation (IPA) in various sectors, Zhou et al [ 58 ] and Lievano-Martinez [ 59 ] offered key ideas and suggested a framework for implementing IPA technology. Using the panel data of pertinent enterprises from 2011 to 2017 and the enterprise panel data of intelligent manufacturing implemented by China’s manufacturing industry from 2014 to 2019, Yang et al [ 60 ] and Liu et al [ 61 ] also looked into the impact of China’s intelligent policy on the performance of listed manufacturing companies. Furthermore, defect diagnosis and early warning for high-end equipment [ 62 ], deep integration of industrial artificial intelligence and the industrial Internet [ 63 ], the local workshop net [ 64 ], and the two-layer distributed hybrid industrial Internet of Things [ 65 ] have all been introduced.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Manufacturing Technology in the Steel Industry of China: A Review

Zhou

et al. 2022

Sensors

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Intelligent manufacturing, defined as the integration of manufacturing with modern information technologies such as 5G, digitalization, networking, and intelligence, has grown in popularity as a means of boosting the productivity, intelligence, and flexibility of traditional manufacturing processes. The steel industry is a necessary support for modern life and economic development, and the Chinese steel industry’s capacity has expanded to roughly half of global production. However, the Chinese steel industry is now confronted with high labor costs, massive carbon emissions, a low level of intelligence, low production efficiency, and unstable quality control. Therefore, China’s steel industry has launched several large-scale intelligent manufacturing initiatives to improve production efficiency, product quality, manual labor intensity, and employee working conditions. Unfortunately, there is no comprehensive overview of intelligent manufacturing in China’s steel industry. We began this research by summarizing the construction goals and overall framework for intelligent manufacturing of the steel industry in China. Following that, we offered a brief review of intelligent manufacturing for China’s steel industry, as well as descriptions of two typical intelligent manufacturing models. Finally, some major technologies employed for intelligent production in China’s steel industry were introduced. This research not only helps to comprehend the development model, essential technologies, and construction techniques of intelligent manufacturing in China’s steel industry, but it also provides vital inspiration for the manufacturing industry’s digital and intelligence updates and quality improvement.

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